Overview

Brought to you by YData

Dataset statistics

Number of variables80
Number of observations1460
Missing cells348
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory923.9 KiB
Average record size in memory648.0 B

Variable types

Categorical53
Numeric26
Boolean1

Alerts

MSZoning is highly imbalanced (56.9%) Imbalance
Street is highly imbalanced (96.2%) Imbalance
Alley is highly imbalanced (74.9%) Imbalance
LandContour is highly imbalanced (68.3%) Imbalance
Utilities is highly imbalanced (99.2%) Imbalance
LandSlope is highly imbalanced (78.8%) Imbalance
Condition1 is highly imbalanced (71.7%) Imbalance
Condition2 is highly imbalanced (96.4%) Imbalance
BldgType is highly imbalanced (59.4%) Imbalance
RoofStyle is highly imbalanced (65.1%) Imbalance
RoofMatl is highly imbalanced (94.4%) Imbalance
ExterCond is highly imbalanced (72.8%) Imbalance
BsmtCond is highly imbalanced (72.4%) Imbalance
BsmtFinType2 is highly imbalanced (67.0%) Imbalance
Heating is highly imbalanced (92.7%) Imbalance
CentralAir is highly imbalanced (65.3%) Imbalance
Electrical is highly imbalanced (80.2%) Imbalance
BsmtHalfBath is highly imbalanced (79.7%) Imbalance
KitchenAbvGr is highly imbalanced (85.7%) Imbalance
Functional is highly imbalanced (81.9%) Imbalance
GarageQual is highly imbalanced (75.5%) Imbalance
GarageCond is highly imbalanced (77.5%) Imbalance
PavedDrive is highly imbalanced (69.9%) Imbalance
PoolQC is highly imbalanced (97.4%) Imbalance
Fence is highly imbalanced (56.5%) Imbalance
MiscFeature is highly imbalanced (89.2%) Imbalance
SaleType is highly imbalanced (75.3%) Imbalance
SaleCondition is highly imbalanced (62.5%) Imbalance
LotFrontage has 259 (17.7%) missing values Missing
GarageYrBlt has 81 (5.5%) missing values Missing
MiscVal is highly skewed (γ1 = 24.47679419) Skewed
MasVnrArea has 861 (59.0%) zeros Zeros
BsmtFinSF1 has 467 (32.0%) zeros Zeros
BsmtFinSF2 has 1293 (88.6%) zeros Zeros
BsmtUnfSF has 118 (8.1%) zeros Zeros
TotalBsmtSF has 37 (2.5%) zeros Zeros
2ndFlrSF has 829 (56.8%) zeros Zeros
LowQualFinSF has 1434 (98.2%) zeros Zeros
GarageArea has 81 (5.5%) zeros Zeros
WoodDeckSF has 761 (52.1%) zeros Zeros
OpenPorchSF has 656 (44.9%) zeros Zeros
EnclosedPorch has 1252 (85.8%) zeros Zeros
3SsnPorch has 1436 (98.4%) zeros Zeros
ScreenPorch has 1344 (92.1%) zeros Zeros
PoolArea has 1453 (99.5%) zeros Zeros
MiscVal has 1408 (96.4%) zeros Zeros

Reproduction

Analysis started2025-01-27 17:26:49.383473
Analysis finished2025-01-27 17:26:56.021510
Duration6.64 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

MSSubClass
Categorical

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
1-STORY 1946 & NEWER ALL STYLES
536 
2-STORY 1946 & NEWER
299 
1-1/2 STORY FINISHED ALL AGES
144 
1-STORY PUD (Planned Unit Development) - 1946 & NEWER
87 
1-STORY 1945 & OLDER
69 
Other values (10)
325 

Length

Max length53
Median length41
Mean length28.017123
Min length11

Characters and Unicode

Total characters40905
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2-STORY 1946 & NEWER
2nd row1-STORY 1946 & NEWER ALL STYLES
3rd row2-STORY 1946 & NEWER
4th row2-STORY 1945 & OLDER
5th row2-STORY 1946 & NEWER

Common Values

ValueCountFrequency (%)
1-STORY 1946 & NEWER ALL STYLES 536
36.7%
2-STORY 1946 & NEWER 299
20.5%
1-1/2 STORY FINISHED ALL AGES 144
 
9.9%
1-STORY PUD (Planned Unit Development) - 1946 & NEWER 87
 
6.0%
1-STORY 1945 & OLDER 69
 
4.7%
2-STORY PUD - 1946 & NEWER 63
 
4.3%
2-STORY 1945 & OLDER 60
 
4.1%
SPLIT OR MULTI-LEVEL 58
 
4.0%
DUPLEX - ALL STYLES AND AGES 52
 
3.6%
2 FAMILY CONVERSION - ALL STYLES AND AGES 30
 
2.1%
Other values (5) 62
 
4.2%

Length

2025-01-27T18:26:56.320142image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1378
17.7%
newer 985
12.6%
1946 985
12.6%
all 794
10.2%
1-story 696
8.9%
styles 618
7.9%
2-story 422
 
5.4%
ages 258
 
3.3%
story 172
 
2.2%
pud 160
 
2.1%
Other values (23) 1333
17.1%

Most occurring characters

ValueCountFrequency (%)
6341
15.5%
E 3393
 
8.3%
S 3062
 
7.5%
L 2729
 
6.7%
R 2522
 
6.2%
1 2138
 
5.2%
T 2072
 
5.1%
Y 1968
 
4.8%
- 1612
 
3.9%
O 1567
 
3.8%
Other values (34) 13501
33.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40905
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6341
15.5%
E 3393
 
8.3%
S 3062
 
7.5%
L 2729
 
6.7%
R 2522
 
6.2%
1 2138
 
5.2%
T 2072
 
5.1%
Y 1968
 
4.8%
- 1612
 
3.9%
O 1567
 
3.8%
Other values (34) 13501
33.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40905
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6341
15.5%
E 3393
 
8.3%
S 3062
 
7.5%
L 2729
 
6.7%
R 2522
 
6.2%
1 2138
 
5.2%
T 2072
 
5.1%
Y 1968
 
4.8%
- 1612
 
3.9%
O 1567
 
3.8%
Other values (34) 13501
33.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40905
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6341
15.5%
E 3393
 
8.3%
S 3062
 
7.5%
L 2729
 
6.7%
R 2522
 
6.2%
1 2138
 
5.2%
T 2072
 
5.1%
Y 1968
 
4.8%
- 1612
 
3.9%
O 1567
 
3.8%
Other values (34) 13501
33.0%

MSZoning
Categorical

Imbalance 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
Residential Low Density
1151 
Residential Medium Density
218 
Floating Village Residential
 
65
Residential High Density
 
16
C (all)
 
10

Length

Max length28
Median length23
Mean length23.571918
Min length7

Characters and Unicode

Total characters34415
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowResidential Low Density
2nd rowResidential Low Density
3rd rowResidential Low Density
4th rowResidential Low Density
5th rowResidential Low Density

Common Values

ValueCountFrequency (%)
Residential Low Density 1151
78.8%
Residential Medium Density 218
 
14.9%
Floating Village Residential 65
 
4.5%
Residential High Density 16
 
1.1%
C (all) 10
 
0.7%

Length

2025-01-27T18:26:56.769630image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T18:26:57.261767image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
residential 1450
33.2%
density 1385
31.7%
low 1151
26.3%
medium 218
 
5.0%
floating 65
 
1.5%
village 65
 
1.5%
high 16
 
0.4%
c 10
 
0.2%
all 10
 
0.2%

Most occurring characters

ValueCountFrequency (%)
i 4649
13.5%
e 4568
13.3%
2910
 
8.5%
n 2900
 
8.4%
t 2900
 
8.4%
s 2835
 
8.2%
d 1668
 
4.8%
l 1665
 
4.8%
a 1590
 
4.6%
R 1450
 
4.2%
Other values (16) 7280
21.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34415
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 4649
13.5%
e 4568
13.3%
2910
 
8.5%
n 2900
 
8.4%
t 2900
 
8.4%
s 2835
 
8.2%
d 1668
 
4.8%
l 1665
 
4.8%
a 1590
 
4.6%
R 1450
 
4.2%
Other values (16) 7280
21.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34415
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 4649
13.5%
e 4568
13.3%
2910
 
8.5%
n 2900
 
8.4%
t 2900
 
8.4%
s 2835
 
8.2%
d 1668
 
4.8%
l 1665
 
4.8%
a 1590
 
4.6%
R 1450
 
4.2%
Other values (16) 7280
21.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34415
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 4649
13.5%
e 4568
13.3%
2910
 
8.5%
n 2900
 
8.4%
t 2900
 
8.4%
s 2835
 
8.2%
d 1668
 
4.8%
l 1665
 
4.8%
a 1590
 
4.6%
R 1450
 
4.2%
Other values (16) 7280
21.2%

LotFrontage
Real number (ℝ)

Missing 

Distinct110
Distinct (%)9.2%
Missing259
Missing (%)17.7%
Infinite0
Infinite (%)0.0%
Mean70.049958
Minimum21
Maximum313
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2025-01-27T18:26:57.811938image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile34
Q159
median69
Q380
95-th percentile107
Maximum313
Range292
Interquartile range (IQR)21

Descriptive statistics

Standard deviation24.284752
Coefficient of variation (CV)0.3466776
Kurtosis17.452867
Mean70.049958
Median Absolute Deviation (MAD)11
Skewness2.1635691
Sum84130
Variance589.74917
MonotonicityNot monotonic
2025-01-27T18:26:58.383539image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 143
 
9.8%
70 70
 
4.8%
80 69
 
4.7%
50 57
 
3.9%
75 53
 
3.6%
65 44
 
3.0%
85 40
 
2.7%
78 25
 
1.7%
90 23
 
1.6%
21 23
 
1.6%
Other values (100) 654
44.8%
(Missing) 259
 
17.7%
ValueCountFrequency (%)
21 23
1.6%
24 19
1.3%
30 6
 
0.4%
32 5
 
0.3%
33 1
 
0.1%
34 10
0.7%
35 9
 
0.6%
36 6
 
0.4%
37 5
 
0.3%
38 1
 
0.1%
ValueCountFrequency (%)
313 2
0.1%
182 1
0.1%
174 2
0.1%
168 1
0.1%
160 1
0.1%
153 1
0.1%
152 1
0.1%
150 1
0.1%
149 1
0.1%
144 1
0.1%

LotArea
Real number (ℝ)

Distinct1073
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10516.828
Minimum1300
Maximum215245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2025-01-27T18:26:58.883435image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1300
5-th percentile3311.7
Q17553.5
median9478.5
Q311601.5
95-th percentile17401.15
Maximum215245
Range213945
Interquartile range (IQR)4048

Descriptive statistics

Standard deviation9981.2649
Coefficient of variation (CV)0.9490756
Kurtosis203.24327
Mean10516.828
Median Absolute Deviation (MAD)1998
Skewness12.207688
Sum15354569
Variance99625650
MonotonicityNot monotonic
2025-01-27T18:26:59.466608image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7200 25
 
1.7%
9600 24
 
1.6%
6000 17
 
1.2%
9000 14
 
1.0%
8400 14
 
1.0%
10800 14
 
1.0%
1680 10
 
0.7%
7500 9
 
0.6%
9100 8
 
0.5%
8125 8
 
0.5%
Other values (1063) 1317
90.2%
ValueCountFrequency (%)
1300 1
 
0.1%
1477 1
 
0.1%
1491 1
 
0.1%
1526 1
 
0.1%
1533 2
 
0.1%
1596 1
 
0.1%
1680 10
0.7%
1869 1
 
0.1%
1890 2
 
0.1%
1920 1
 
0.1%
ValueCountFrequency (%)
215245 1
0.1%
164660 1
0.1%
159000 1
0.1%
115149 1
0.1%
70761 1
0.1%
63887 1
0.1%
57200 1
0.1%
53504 1
0.1%
53227 1
0.1%
53107 1
0.1%

Street
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
Paved
1454 
Gravel
 
6

Length

Max length6
Median length5
Mean length5.0041096
Min length5

Characters and Unicode

Total characters7306
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPaved
2nd rowPaved
3rd rowPaved
4th rowPaved
5th rowPaved

Common Values

ValueCountFrequency (%)
Paved 1454
99.6%
Gravel 6
 
0.4%

Length

2025-01-27T18:27:00.039016image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T18:27:00.737065image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
paved 1454
99.6%
gravel 6
 
0.4%

Most occurring characters

ValueCountFrequency (%)
a 1460
20.0%
v 1460
20.0%
e 1460
20.0%
P 1454
19.9%
d 1454
19.9%
G 6
 
0.1%
r 6
 
0.1%
l 6
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7306
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1460
20.0%
v 1460
20.0%
e 1460
20.0%
P 1454
19.9%
d 1454
19.9%
G 6
 
0.1%
r 6
 
0.1%
l 6
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7306
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1460
20.0%
v 1460
20.0%
e 1460
20.0%
P 1454
19.9%
d 1454
19.9%
G 6
 
0.1%
r 6
 
0.1%
l 6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7306
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1460
20.0%
v 1460
20.0%
e 1460
20.0%
P 1454
19.9%
d 1454
19.9%
G 6
 
0.1%
r 6
 
0.1%
l 6
 
0.1%

Alley
Categorical

Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
nan
1369 
Gravel
 
50
Paved
 
41

Length

Max length6
Median length3
Mean length3.1589041
Min length3

Characters and Unicode

Total characters4612
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan

Common Values

ValueCountFrequency (%)
nan 1369
93.8%
Gravel 50
 
3.4%
Paved 41
 
2.8%

Length

2025-01-27T18:27:01.336215image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T18:27:01.772014image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
nan 1369
93.8%
gravel 50
 
3.4%
paved 41
 
2.8%

Most occurring characters

ValueCountFrequency (%)
n 2738
59.4%
a 1460
31.7%
v 91
 
2.0%
e 91
 
2.0%
G 50
 
1.1%
r 50
 
1.1%
l 50
 
1.1%
P 41
 
0.9%
d 41
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4612
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 2738
59.4%
a 1460
31.7%
v 91
 
2.0%
e 91
 
2.0%
G 50
 
1.1%
r 50
 
1.1%
l 50
 
1.1%
P 41
 
0.9%
d 41
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4612
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 2738
59.4%
a 1460
31.7%
v 91
 
2.0%
e 91
 
2.0%
G 50
 
1.1%
r 50
 
1.1%
l 50
 
1.1%
P 41
 
0.9%
d 41
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4612
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 2738
59.4%
a 1460
31.7%
v 91
 
2.0%
e 91
 
2.0%
G 50
 
1.1%
r 50
 
1.1%
l 50
 
1.1%
P 41
 
0.9%
d 41
 
0.9%

LotShape
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
Regular
925 
Slightly irregular
484 
Moderately Irregular
 
41
Irregular
 
10

Length

Max length20
Median length7
Mean length11.025342
Min length7

Characters and Unicode

Total characters16097
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRegular
2nd rowRegular
3rd rowSlightly irregular
4th rowSlightly irregular
5th rowSlightly irregular

Common Values

ValueCountFrequency (%)
Regular 925
63.4%
Slightly irregular 484
33.2%
Moderately Irregular 41
 
2.8%
Irregular 10
 
0.7%

Length

2025-01-27T18:27:02.335230image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T18:27:02.781733image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
regular 925
46.6%
irregular 535
27.0%
slightly 484
24.4%
moderately 41
 
2.1%

Most occurring characters

ValueCountFrequency (%)
r 2571
16.0%
l 2469
15.3%
g 1944
12.1%
e 1542
9.6%
a 1501
9.3%
u 1460
9.1%
i 968
 
6.0%
R 925
 
5.7%
t 525
 
3.3%
y 525
 
3.3%
Other values (7) 1667
10.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16097
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 2571
16.0%
l 2469
15.3%
g 1944
12.1%
e 1542
9.6%
a 1501
9.3%
u 1460
9.1%
i 968
 
6.0%
R 925
 
5.7%
t 525
 
3.3%
y 525
 
3.3%
Other values (7) 1667
10.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16097
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 2571
16.0%
l 2469
15.3%
g 1944
12.1%
e 1542
9.6%
a 1501
9.3%
u 1460
9.1%
i 968
 
6.0%
R 925
 
5.7%
t 525
 
3.3%
y 525
 
3.3%
Other values (7) 1667
10.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16097
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 2571
16.0%
l 2469
15.3%
g 1944
12.1%
e 1542
9.6%
a 1501
9.3%
u 1460
9.1%
i 968
 
6.0%
R 925
 
5.7%
t 525
 
3.3%
y 525
 
3.3%
Other values (7) 1667
10.4%

LandContour
Categorical

Imbalance 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
Near Flat/Level
1311 
Banked - Quick and significant rise from street grade to building
 
63
Hillside - Significant slope from side to side
 
50
Depression
 
36

Length

Max length65
Median length15
Mean length18.09589
Min length10

Characters and Unicode

Total characters26420
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNear Flat/Level
2nd rowNear Flat/Level
3rd rowNear Flat/Level
4th rowNear Flat/Level
5th rowNear Flat/Level

Common Values

ValueCountFrequency (%)
Near Flat/Level 1311
89.8%
Banked - Quick and significant rise from street grade to building 63
 
4.3%
Hillside - Significant slope from side to side 50
 
3.4%
Depression 36
 
2.5%

Length

2025-01-27T18:27:03.366984image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T18:27:04.105498image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
near 1311
35.0%
flat/level 1311
35.0%
significant 113
 
3.0%
to 113
 
3.0%
from 113
 
3.0%
113
 
3.0%
side 100
 
2.7%
and 63
 
1.7%
quick 63
 
1.7%
rise 63
 
1.7%
Other values (7) 388
 
10.3%

Most occurring characters

ValueCountFrequency (%)
e 4520
17.1%
a 2924
11.1%
l 2835
10.7%
2291
8.7%
t 1663
 
6.3%
r 1649
 
6.2%
N 1311
 
5.0%
F 1311
 
5.0%
/ 1311
 
5.0%
L 1311
 
5.0%
Other values (20) 5294
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26420
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 4520
17.1%
a 2924
11.1%
l 2835
10.7%
2291
8.7%
t 1663
 
6.3%
r 1649
 
6.2%
N 1311
 
5.0%
F 1311
 
5.0%
/ 1311
 
5.0%
L 1311
 
5.0%
Other values (20) 5294
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26420
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 4520
17.1%
a 2924
11.1%
l 2835
10.7%
2291
8.7%
t 1663
 
6.3%
r 1649
 
6.2%
N 1311
 
5.0%
F 1311
 
5.0%
/ 1311
 
5.0%
L 1311
 
5.0%
Other values (20) 5294
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26420
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 4520
17.1%
a 2924
11.1%
l 2835
10.7%
2291
8.7%
t 1663
 
6.3%
r 1649
 
6.2%
N 1311
 
5.0%
F 1311
 
5.0%
/ 1311
 
5.0%
L 1311
 
5.0%
Other values (20) 5294
20.0%

Utilities
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
All public Utilities (E,G,W,& S)
1459 
Electricity and Gas Only
 
1

Length

Max length32
Median length32
Mean length31.994521
Min length24

Characters and Unicode

Total characters46712
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowAll public Utilities (E,G,W,& S)
2nd rowAll public Utilities (E,G,W,& S)
3rd rowAll public Utilities (E,G,W,& S)
4th rowAll public Utilities (E,G,W,& S)
5th rowAll public Utilities (E,G,W,& S)

Common Values

ValueCountFrequency (%)
All public Utilities (E,G,W,& S) 1459
99.9%
Electricity and Gas Only 1
 
0.1%

Length

2025-01-27T18:27:05.278064image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T18:27:06.203893image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
all 1459
20.0%
public 1459
20.0%
utilities 1459
20.0%
e,g,w 1459
20.0%
s 1459
20.0%
electricity 1
 
< 0.1%
and 1
 
< 0.1%
gas 1
 
< 0.1%
only 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
5839
 
12.5%
i 5838
 
12.5%
l 5838
 
12.5%
, 4377
 
9.4%
t 2920
 
6.3%
c 1461
 
3.1%
E 1460
 
3.1%
e 1460
 
3.1%
s 1460
 
3.1%
G 1460
 
3.1%
Other values (16) 14599
31.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 46712
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5839
 
12.5%
i 5838
 
12.5%
l 5838
 
12.5%
, 4377
 
9.4%
t 2920
 
6.3%
c 1461
 
3.1%
E 1460
 
3.1%
e 1460
 
3.1%
s 1460
 
3.1%
G 1460
 
3.1%
Other values (16) 14599
31.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 46712
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5839
 
12.5%
i 5838
 
12.5%
l 5838
 
12.5%
, 4377
 
9.4%
t 2920
 
6.3%
c 1461
 
3.1%
E 1460
 
3.1%
e 1460
 
3.1%
s 1460
 
3.1%
G 1460
 
3.1%
Other values (16) 14599
31.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 46712
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5839
 
12.5%
i 5838
 
12.5%
l 5838
 
12.5%
, 4377
 
9.4%
t 2920
 
6.3%
c 1461
 
3.1%
E 1460
 
3.1%
e 1460
 
3.1%
s 1460
 
3.1%
G 1460
 
3.1%
Other values (16) 14599
31.3%

LotConfig
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
Inside lot
1052 
Corner lot
263 
Cul-de-sac
 
94
Frontage on 2 sides of property
 
47
Frontage on 3 sides of property
 
4

Length

Max length31
Median length10
Mean length10.733562
Min length10

Characters and Unicode

Total characters15671
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInside lot
2nd rowFrontage on 2 sides of property
3rd rowInside lot
4th rowCorner lot
5th rowFrontage on 2 sides of property

Common Values

ValueCountFrequency (%)
Inside lot 1052
72.1%
Corner lot 263
 
18.0%
Cul-de-sac 94
 
6.4%
Frontage on 2 sides of property 47
 
3.2%
Frontage on 3 sides of property 4
 
0.3%

Length

2025-01-27T18:27:06.862442image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T18:27:07.646403image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
lot 1315
43.4%
inside 1052
34.7%
corner 263
 
8.7%
cul-de-sac 94
 
3.1%
frontage 51
 
1.7%
on 51
 
1.7%
sides 51
 
1.7%
of 51
 
1.7%
property 51
 
1.7%
2 47
 
1.6%

Most occurring characters

ValueCountFrequency (%)
o 1782
11.4%
1570
10.0%
e 1562
10.0%
n 1417
9.0%
t 1417
9.0%
l 1409
9.0%
s 1248
8.0%
d 1197
7.6%
i 1103
7.0%
I 1052
6.7%
Other values (13) 1914
12.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15671
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1782
11.4%
1570
10.0%
e 1562
10.0%
n 1417
9.0%
t 1417
9.0%
l 1409
9.0%
s 1248
8.0%
d 1197
7.6%
i 1103
7.0%
I 1052
6.7%
Other values (13) 1914
12.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15671
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1782
11.4%
1570
10.0%
e 1562
10.0%
n 1417
9.0%
t 1417
9.0%
l 1409
9.0%
s 1248
8.0%
d 1197
7.6%
i 1103
7.0%
I 1052
6.7%
Other values (13) 1914
12.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15671
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1782
11.4%
1570
10.0%
e 1562
10.0%
n 1417
9.0%
t 1417
9.0%
l 1409
9.0%
s 1248
8.0%
d 1197
7.6%
i 1103
7.0%
I 1052
6.7%
Other values (13) 1914
12.2%

LandSlope
Categorical

Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
Gentle slope
1382 
Moderate Slope
 
65
Severe Slope
 
13

Length

Max length14
Median length12
Mean length12.089041
Min length12

Characters and Unicode

Total characters17650
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGentle slope
2nd rowGentle slope
3rd rowGentle slope
4th rowGentle slope
5th rowGentle slope

Common Values

ValueCountFrequency (%)
Gentle slope 1382
94.7%
Moderate Slope 65
 
4.5%
Severe Slope 13
 
0.9%

Length

2025-01-27T18:27:08.796360image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T18:27:09.500997image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
slope 1460
50.0%
gentle 1382
47.3%
moderate 65
 
2.2%
severe 13
 
0.4%

Most occurring characters

ValueCountFrequency (%)
e 4393
24.9%
l 2842
16.1%
o 1525
 
8.6%
1460
 
8.3%
p 1460
 
8.3%
t 1447
 
8.2%
G 1382
 
7.8%
n 1382
 
7.8%
s 1382
 
7.8%
S 91
 
0.5%
Other values (5) 286
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17650
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 4393
24.9%
l 2842
16.1%
o 1525
 
8.6%
1460
 
8.3%
p 1460
 
8.3%
t 1447
 
8.2%
G 1382
 
7.8%
n 1382
 
7.8%
s 1382
 
7.8%
S 91
 
0.5%
Other values (5) 286
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17650
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 4393
24.9%
l 2842
16.1%
o 1525
 
8.6%
1460
 
8.3%
p 1460
 
8.3%
t 1447
 
8.2%
G 1382
 
7.8%
n 1382
 
7.8%
s 1382
 
7.8%
S 91
 
0.5%
Other values (5) 286
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17650
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 4393
24.9%
l 2842
16.1%
o 1525
 
8.6%
1460
 
8.3%
p 1460
 
8.3%
t 1447
 
8.2%
G 1382
 
7.8%
n 1382
 
7.8%
s 1382
 
7.8%
S 91
 
0.5%
Other values (5) 286
 
1.6%

Neighborhood
Categorical

Distinct25
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
North Ames
225 
College Creek
150 
Old Town
113 
Edwards
100 
Somerset
86 
Other values (20)
786 

Length

Max length37
Median length19
Mean length10.899315
Min length6

Characters and Unicode

Total characters15913
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCollege Creek
2nd rowVeenker
3rd rowCollege Creek
4th rowCrawford
5th rowNorthridge

Common Values

ValueCountFrequency (%)
North Ames 225
15.4%
College Creek 150
 
10.3%
Old Town 113
 
7.7%
Edwards 100
 
6.8%
Somerset 86
 
5.9%
Gilbert 79
 
5.4%
Northridge Heights 77
 
5.3%
Sawyer 74
 
5.1%
Northwest Ames 73
 
5.0%
Sawyer West 59
 
4.0%
Other values (15) 424
29.0%

Length

2025-01-27T18:27:10.177252image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ames 298
 
11.7%
north 225
 
8.8%
creek 178
 
7.0%
college 150
 
5.9%
sawyer 133
 
5.2%
northridge 118
 
4.6%
old 113
 
4.4%
town 113
 
4.4%
edwards 100
 
3.9%
heights 94
 
3.7%
Other values (29) 1029
40.3%

Most occurring characters

ValueCountFrequency (%)
e 2042
 
12.8%
r 1447
 
9.1%
o 1233
 
7.7%
1091
 
6.9%
t 1034
 
6.5%
s 820
 
5.2%
l 780
 
4.9%
d 685
 
4.3%
a 632
 
4.0%
h 593
 
3.7%
Other values (31) 5556
34.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15913
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2042
 
12.8%
r 1447
 
9.1%
o 1233
 
7.7%
1091
 
6.9%
t 1034
 
6.5%
s 820
 
5.2%
l 780
 
4.9%
d 685
 
4.3%
a 632
 
4.0%
h 593
 
3.7%
Other values (31) 5556
34.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15913
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2042
 
12.8%
r 1447
 
9.1%
o 1233
 
7.7%
1091
 
6.9%
t 1034
 
6.5%
s 820
 
5.2%
l 780
 
4.9%
d 685
 
4.3%
a 632
 
4.0%
h 593
 
3.7%
Other values (31) 5556
34.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15913
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2042
 
12.8%
r 1447
 
9.1%
o 1233
 
7.7%
1091
 
6.9%
t 1034
 
6.5%
s 820
 
5.2%
l 780
 
4.9%
d 685
 
4.3%
a 632
 
4.0%
h 593
 
3.7%
Other values (31) 5556
34.9%

Condition1
Categorical

Imbalance 

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
Normal
1260 
Adjacent to feeder street
 
81
Adjacent to arterial street
 
48
Adjacent to North-South Railroad
 
26
Near positive off-site feature--park, greenbelt, etc.
 
19
Other values (4)
 
26

Length

Max length53
Median length6
Mean length9.3006849
Min length6

Characters and Unicode

Total characters13579
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNormal
2nd rowAdjacent to feeder street
3rd rowNormal
4th rowNormal
5th rowNormal

Common Values

ValueCountFrequency (%)
Normal 1260
86.3%
Adjacent to feeder street 81
 
5.5%
Adjacent to arterial street 48
 
3.3%
Adjacent to North-South Railroad 26
 
1.8%
Near positive off-site feature--park, greenbelt, etc. 19
 
1.3%
Adjacent to East-West Railroad 11
 
0.8%
Adjacent to postive off-site feature 8
 
0.5%
Within 200' of North-South Railroad 5
 
0.3%
Within 200' of East-West Railroad 2
 
0.1%

Length

2025-01-27T18:27:10.837948image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T18:27:11.652571image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
normal 1260
59.6%
adjacent 174
 
8.2%
to 174
 
8.2%
street 129
 
6.1%
feeder 81
 
3.8%
arterial 48
 
2.3%
railroad 44
 
2.1%
north-south 31
 
1.5%
off-site 27
 
1.3%
positive 19
 
0.9%
Other values (10) 126
 
6.0%

Most occurring characters

ValueCountFrequency (%)
r 1725
12.7%
a 1696
12.5%
o 1601
11.8%
l 1371
10.1%
N 1310
9.6%
m 1260
9.3%
e 939
6.9%
t 868
6.4%
653
 
4.8%
d 299
 
2.2%
Other values (24) 1857
13.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13579
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 1725
12.7%
a 1696
12.5%
o 1601
11.8%
l 1371
10.1%
N 1310
9.6%
m 1260
9.3%
e 939
6.9%
t 868
6.4%
653
 
4.8%
d 299
 
2.2%
Other values (24) 1857
13.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13579
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 1725
12.7%
a 1696
12.5%
o 1601
11.8%
l 1371
10.1%
N 1310
9.6%
m 1260
9.3%
e 939
6.9%
t 868
6.4%
653
 
4.8%
d 299
 
2.2%
Other values (24) 1857
13.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13579
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 1725
12.7%
a 1696
12.5%
o 1601
11.8%
l 1371
10.1%
N 1310
9.6%
m 1260
9.3%
e 939
6.9%
t 868
6.4%
653
 
4.8%
d 299
 
2.2%
Other values (24) 1857
13.7%

Condition2
Categorical

Imbalance 

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
Normal
1445 
Adjacent to feeder street
 
6
Adjacent to arterial street
 
2
Within 200' of North-South Railroad
 
2
Near positive off-site feature--park, greenbelt, etc.
 
2
Other values (3)
 
3

Length

Max length53
Median length6
Mean length6.2657534
Min length6

Characters and Unicode

Total characters9148
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st rowNormal
2nd rowNormal
3rd rowNormal
4th rowNormal
5th rowNormal

Common Values

ValueCountFrequency (%)
Normal 1445
99.0%
Adjacent to feeder street 6
 
0.4%
Adjacent to arterial street 2
 
0.1%
Within 200' of North-South Railroad 2
 
0.1%
Near positive off-site feature--park, greenbelt, etc. 2
 
0.1%
Adjacent to postive off-site feature 1
 
0.1%
Adjacent to North-South Railroad 1
 
0.1%
Adjacent to East-West Railroad 1
 
0.1%

Length

2025-01-27T18:27:12.568008image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T18:27:13.046000image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
normal 1445
95.6%
adjacent 11
 
0.7%
to 11
 
0.7%
street 8
 
0.5%
feeder 6
 
0.4%
railroad 4
 
0.3%
north-south 3
 
0.2%
off-site 3
 
0.2%
within 2
 
0.1%
200 2
 
0.1%
Other values (10) 17
 
1.1%

Most occurring characters

ValueCountFrequency (%)
r 1479
16.2%
a 1476
16.1%
o 1474
16.1%
l 1453
15.9%
N 1450
15.9%
m 1445
15.8%
e 70
 
0.8%
t 63
 
0.7%
52
 
0.6%
d 21
 
0.2%
Other values (24) 165
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9148
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 1479
16.2%
a 1476
16.1%
o 1474
16.1%
l 1453
15.9%
N 1450
15.9%
m 1445
15.8%
e 70
 
0.8%
t 63
 
0.7%
52
 
0.6%
d 21
 
0.2%
Other values (24) 165
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9148
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 1479
16.2%
a 1476
16.1%
o 1474
16.1%
l 1453
15.9%
N 1450
15.9%
m 1445
15.8%
e 70
 
0.8%
t 63
 
0.7%
52
 
0.6%
d 21
 
0.2%
Other values (24) 165
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9148
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 1479
16.2%
a 1476
16.1%
o 1474
16.1%
l 1453
15.9%
N 1450
15.9%
m 1445
15.8%
e 70
 
0.8%
t 63
 
0.7%
52
 
0.6%
d 21
 
0.2%
Other values (24) 165
 
1.8%

BldgType
Categorical

Imbalance 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
Single-family Detached
1220 
Townhouse End Unit
 
114
Duplex
 
52
Twnhs
 
43
2fmCon
 
31

Length

Max length22
Median length22
Mean length20.277397
Min length5

Characters and Unicode

Total characters29605
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle-family Detached
2nd rowSingle-family Detached
3rd rowSingle-family Detached
4th rowSingle-family Detached
5th rowSingle-family Detached

Common Values

ValueCountFrequency (%)
Single-family Detached 1220
83.6%
Townhouse End Unit 114
 
7.8%
Duplex 52
 
3.6%
Twnhs 43
 
2.9%
2fmCon 31
 
2.1%

Length

2025-01-27T18:27:13.692469image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T18:27:14.031506image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
single-family 1220
42.0%
detached 1220
42.0%
townhouse 114
 
3.9%
end 114
 
3.9%
unit 114
 
3.9%
duplex 52
 
1.8%
twnhs 43
 
1.5%
2fmcon 31
 
1.1%

Most occurring characters

ValueCountFrequency (%)
e 3826
 
12.9%
i 2554
 
8.6%
l 2492
 
8.4%
a 2440
 
8.2%
n 1636
 
5.5%
1448
 
4.9%
h 1377
 
4.7%
t 1334
 
4.5%
d 1334
 
4.5%
D 1272
 
4.3%
Other values (18) 9892
33.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 29605
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 3826
 
12.9%
i 2554
 
8.6%
l 2492
 
8.4%
a 2440
 
8.2%
n 1636
 
5.5%
1448
 
4.9%
h 1377
 
4.7%
t 1334
 
4.5%
d 1334
 
4.5%
D 1272
 
4.3%
Other values (18) 9892
33.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 29605
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 3826
 
12.9%
i 2554
 
8.6%
l 2492
 
8.4%
a 2440
 
8.2%
n 1636
 
5.5%
1448
 
4.9%
h 1377
 
4.7%
t 1334
 
4.5%
d 1334
 
4.5%
D 1272
 
4.3%
Other values (18) 9892
33.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 29605
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 3826
 
12.9%
i 2554
 
8.6%
l 2492
 
8.4%
a 2440
 
8.2%
n 1636
 
5.5%
1448
 
4.9%
h 1377
 
4.7%
t 1334
 
4.5%
d 1334
 
4.5%
D 1272
 
4.3%
Other values (18) 9892
33.4%

HouseStyle
Categorical

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
One story
726 
2Story
445 
1.5Fin
154 
SLvl
 
65
SFoyer
 
37
Other values (3)
 
33

Length

Max length9
Median length6
Mean length7.4027397
Min length4

Characters and Unicode

Total characters10808
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2Story
2nd rowOne story
3rd row2Story
4th row2Story
5th row2Story

Common Values

ValueCountFrequency (%)
One story 726
49.7%
2Story 445
30.5%
1.5Fin 154
 
10.5%
SLvl 65
 
4.5%
SFoyer 37
 
2.5%
1.5Unf 14
 
1.0%
2.5Unf 11
 
0.8%
2.5Fin 8
 
0.5%

Length

2025-01-27T18:27:14.385949image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T18:27:14.925259image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
one 726
33.2%
story 726
33.2%
2story 445
20.4%
1.5fin 154
 
7.0%
slvl 65
 
3.0%
sfoyer 37
 
1.7%
1.5unf 14
 
0.6%
2.5unf 11
 
0.5%
2.5fin 8
 
0.4%

Most occurring characters

ValueCountFrequency (%)
o 1208
11.2%
r 1208
11.2%
y 1208
11.2%
t 1171
10.8%
n 913
8.4%
e 763
7.1%
O 726
6.7%
726
6.7%
s 726
6.7%
S 547
 
5.1%
Other values (11) 1612
14.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10808
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1208
11.2%
r 1208
11.2%
y 1208
11.2%
t 1171
10.8%
n 913
8.4%
e 763
7.1%
O 726
6.7%
726
6.7%
s 726
6.7%
S 547
 
5.1%
Other values (11) 1612
14.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10808
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1208
11.2%
r 1208
11.2%
y 1208
11.2%
t 1171
10.8%
n 913
8.4%
e 763
7.1%
O 726
6.7%
726
6.7%
s 726
6.7%
S 547
 
5.1%
Other values (11) 1612
14.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10808
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1208
11.2%
r 1208
11.2%
y 1208
11.2%
t 1171
10.8%
n 913
8.4%
e 763
7.1%
O 726
6.7%
726
6.7%
s 726
6.7%
S 547
 
5.1%
Other values (11) 1612
14.9%

OverallQual
Categorical

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
Average
397 
Above Average
374 
Good
319 
Very Good
168 
Below Average
116 
Other values (5)
86 

Length

Max length14
Median length13
Mean length8.6890411
Min length4

Characters and Unicode

Total characters12686
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGood
2nd rowAbove Average
3rd rowGood
4th rowGood
5th rowVery Good

Common Values

ValueCountFrequency (%)
Average 397
27.2%
Above Average 374
25.6%
Good 319
21.8%
Very Good 168
11.5%
Below Average 116
 
7.9%
Excellent 43
 
2.9%
Fair 20
 
1.4%
Very Excellent 18
 
1.2%
Poor 3
 
0.2%
Very Poor 2
 
0.1%

Length

2025-01-27T18:27:15.332195image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T18:27:15.671710image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
average 887
41.5%
good 487
22.8%
above 374
17.5%
very 188
 
8.8%
below 116
 
5.4%
excellent 61
 
2.9%
fair 20
 
0.9%
poor 5
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e 2574
20.3%
o 1474
11.6%
A 1261
9.9%
v 1261
9.9%
r 1100
8.7%
a 907
 
7.1%
g 887
 
7.0%
678
 
5.3%
G 487
 
3.8%
d 487
 
3.8%
Other values (14) 1570
12.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12686
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2574
20.3%
o 1474
11.6%
A 1261
9.9%
v 1261
9.9%
r 1100
8.7%
a 907
 
7.1%
g 887
 
7.0%
678
 
5.3%
G 487
 
3.8%
d 487
 
3.8%
Other values (14) 1570
12.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12686
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2574
20.3%
o 1474
11.6%
A 1261
9.9%
v 1261
9.9%
r 1100
8.7%
a 907
 
7.1%
g 887
 
7.0%
678
 
5.3%
G 487
 
3.8%
d 487
 
3.8%
Other values (14) 1570
12.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12686
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2574
20.3%
o 1474
11.6%
A 1261
9.9%
v 1261
9.9%
r 1100
8.7%
a 907
 
7.1%
g 887
 
7.0%
678
 
5.3%
G 487
 
3.8%
d 487
 
3.8%
Other values (14) 1570
12.4%

OverallCond
Categorical

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
Average
821 
Above Average
252 
Good
205 
Very Good
 
72
Below Average
 
57
Other values (4)
 
53

Length

Max length13
Median length7
Mean length7.9171233
Min length4

Characters and Unicode

Total characters11559
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowAverage
2nd rowVery Good
3rd rowAverage
4th rowAverage
5th rowAverage

Common Values

ValueCountFrequency (%)
Average 821
56.2%
Above Average 252
 
17.3%
Good 205
 
14.0%
Very Good 72
 
4.9%
Below Average 57
 
3.9%
Fair 25
 
1.7%
Excellent 22
 
1.5%
Poor 5
 
0.3%
Very Poor 1
 
0.1%

Length

2025-01-27T18:27:16.067752image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T18:27:16.387790image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
average 1130
61.3%
good 277
 
15.0%
above 252
 
13.7%
very 73
 
4.0%
below 57
 
3.1%
fair 25
 
1.4%
excellent 22
 
1.2%
poor 6
 
0.3%

Most occurring characters

ValueCountFrequency (%)
e 2686
23.2%
A 1382
12.0%
v 1382
12.0%
r 1234
10.7%
a 1155
10.0%
g 1130
9.8%
o 875
 
7.6%
382
 
3.3%
G 277
 
2.4%
d 277
 
2.4%
Other values (14) 779
 
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11559
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2686
23.2%
A 1382
12.0%
v 1382
12.0%
r 1234
10.7%
a 1155
10.0%
g 1130
9.8%
o 875
 
7.6%
382
 
3.3%
G 277
 
2.4%
d 277
 
2.4%
Other values (14) 779
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11559
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2686
23.2%
A 1382
12.0%
v 1382
12.0%
r 1234
10.7%
a 1155
10.0%
g 1130
9.8%
o 875
 
7.6%
382
 
3.3%
G 277
 
2.4%
d 277
 
2.4%
Other values (14) 779
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11559
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2686
23.2%
A 1382
12.0%
v 1382
12.0%
r 1234
10.7%
a 1155
10.0%
g 1130
9.8%
o 875
 
7.6%
382
 
3.3%
G 277
 
2.4%
d 277
 
2.4%
Other values (14) 779
 
6.7%

YearBuilt
Real number (ℝ)

Distinct112
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1971.2678
Minimum1872
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2025-01-27T18:27:16.827665image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1872
5-th percentile1916
Q11954
median1973
Q32000
95-th percentile2007
Maximum2010
Range138
Interquartile range (IQR)46

Descriptive statistics

Standard deviation30.202904
Coefficient of variation (CV)0.015321563
Kurtosis-0.43955194
Mean1971.2678
Median Absolute Deviation (MAD)25
Skewness-0.61346117
Sum2878051
Variance912.21541
MonotonicityNot monotonic
2025-01-27T18:27:17.305937image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2006 67
 
4.6%
2005 64
 
4.4%
2004 54
 
3.7%
2007 49
 
3.4%
2003 45
 
3.1%
1976 33
 
2.3%
1977 32
 
2.2%
1920 30
 
2.1%
1959 26
 
1.8%
1998 25
 
1.7%
Other values (102) 1035
70.9%
ValueCountFrequency (%)
1872 1
 
0.1%
1875 1
 
0.1%
1880 4
 
0.3%
1882 1
 
0.1%
1885 2
 
0.1%
1890 2
 
0.1%
1892 2
 
0.1%
1893 1
 
0.1%
1898 1
 
0.1%
1900 10
0.7%
ValueCountFrequency (%)
2010 1
 
0.1%
2009 18
 
1.2%
2008 23
 
1.6%
2007 49
3.4%
2006 67
4.6%
2005 64
4.4%
2004 54
3.7%
2003 45
3.1%
2002 23
 
1.6%
2001 20
 
1.4%

YearRemodAdd
Real number (ℝ)

Distinct61
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1984.8658
Minimum1950
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2025-01-27T18:27:17.815309image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1950
5-th percentile1950
Q11967
median1994
Q32004
95-th percentile2007
Maximum2010
Range60
Interquartile range (IQR)37

Descriptive statistics

Standard deviation20.645407
Coefficient of variation (CV)0.010401412
Kurtosis-1.2722452
Mean1984.8658
Median Absolute Deviation (MAD)13
Skewness-0.503562
Sum2897904
Variance426.23282
MonotonicityNot monotonic
2025-01-27T18:27:18.295470image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1950 178
 
12.2%
2006 97
 
6.6%
2007 76
 
5.2%
2005 73
 
5.0%
2004 62
 
4.2%
2000 55
 
3.8%
2003 51
 
3.5%
2002 48
 
3.3%
2008 40
 
2.7%
1996 36
 
2.5%
Other values (51) 744
51.0%
ValueCountFrequency (%)
1950 178
12.2%
1951 4
 
0.3%
1952 5
 
0.3%
1953 10
 
0.7%
1954 14
 
1.0%
1955 9
 
0.6%
1956 10
 
0.7%
1957 9
 
0.6%
1958 15
 
1.0%
1959 18
 
1.2%
ValueCountFrequency (%)
2010 6
 
0.4%
2009 23
 
1.6%
2008 40
2.7%
2007 76
5.2%
2006 97
6.6%
2005 73
5.0%
2004 62
4.2%
2003 51
3.5%
2002 48
3.3%
2001 21
 
1.4%

RoofStyle
Categorical

Imbalance 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
Gable
1141 
Hip
286 
Flat
 
13
Gabrel (Barn)
 
11
Mansard
 
7

Length

Max length13
Median length5
Mean length4.6678082
Min length3

Characters and Unicode

Total characters6815
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGable
2nd rowGable
3rd rowGable
4th rowGable
5th rowGable

Common Values

ValueCountFrequency (%)
Gable 1141
78.2%
Hip 286
 
19.6%
Flat 13
 
0.9%
Gabrel (Barn) 11
 
0.8%
Mansard 7
 
0.5%
Shed 2
 
0.1%

Length

2025-01-27T18:27:18.700367image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T18:27:19.022126image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
gable 1141
77.6%
hip 286
 
19.4%
flat 13
 
0.9%
gabrel 11
 
0.7%
barn 11
 
0.7%
mansard 7
 
0.5%
shed 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
a 1190
17.5%
l 1165
17.1%
e 1154
16.9%
G 1152
16.9%
b 1152
16.9%
H 286
 
4.2%
i 286
 
4.2%
p 286
 
4.2%
r 29
 
0.4%
n 18
 
0.3%
Other values (11) 97
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6815
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1190
17.5%
l 1165
17.1%
e 1154
16.9%
G 1152
16.9%
b 1152
16.9%
H 286
 
4.2%
i 286
 
4.2%
p 286
 
4.2%
r 29
 
0.4%
n 18
 
0.3%
Other values (11) 97
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6815
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1190
17.5%
l 1165
17.1%
e 1154
16.9%
G 1152
16.9%
b 1152
16.9%
H 286
 
4.2%
i 286
 
4.2%
p 286
 
4.2%
r 29
 
0.4%
n 18
 
0.3%
Other values (11) 97
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6815
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1190
17.5%
l 1165
17.1%
e 1154
16.9%
G 1152
16.9%
b 1152
16.9%
H 286
 
4.2%
i 286
 
4.2%
p 286
 
4.2%
r 29
 
0.4%
n 18
 
0.3%
Other values (11) 97
 
1.4%

RoofMatl
Categorical

Imbalance 

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
Standard (Composite) Shingle
1434 
Gravel & Tar
 
11
Wood Shingles
 
6
Wood Shakes
 
5
Metal
 
1
Other values (3)
 
3

Length

Max length28
Median length28
Mean length27.70274
Min length4

Characters and Unicode

Total characters40446
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.3%

Sample

1st rowStandard (Composite) Shingle
2nd rowStandard (Composite) Shingle
3rd rowStandard (Composite) Shingle
4th rowStandard (Composite) Shingle
5th rowStandard (Composite) Shingle

Common Values

ValueCountFrequency (%)
Standard (Composite) Shingle 1434
98.2%
Gravel & Tar 11
 
0.8%
Wood Shingles 6
 
0.4%
Wood Shakes 5
 
0.3%
Metal 1
 
0.1%
Membrane 1
 
0.1%
Roll 1
 
0.1%
Clay or Tile 1
 
0.1%

Length

2025-01-27T18:27:19.408732image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T18:27:19.868513image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
standard 1434
32.9%
composite 1434
32.9%
shingle 1434
32.9%
gravel 11
 
0.3%
11
 
0.3%
tar 11
 
0.3%
wood 11
 
0.3%
shingles 6
 
0.1%
shakes 5
 
0.1%
metal 1
 
< 0.1%
Other values (5) 5
 
0.1%

Most occurring characters

ValueCountFrequency (%)
2903
 
7.2%
a 2898
 
7.2%
e 2894
 
7.2%
o 2892
 
7.2%
S 2879
 
7.1%
d 2879
 
7.1%
i 2875
 
7.1%
n 2875
 
7.1%
t 2869
 
7.1%
r 1458
 
3.6%
Other values (19) 13024
32.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40446
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2903
 
7.2%
a 2898
 
7.2%
e 2894
 
7.2%
o 2892
 
7.2%
S 2879
 
7.1%
d 2879
 
7.1%
i 2875
 
7.1%
n 2875
 
7.1%
t 2869
 
7.1%
r 1458
 
3.6%
Other values (19) 13024
32.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40446
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2903
 
7.2%
a 2898
 
7.2%
e 2894
 
7.2%
o 2892
 
7.2%
S 2879
 
7.1%
d 2879
 
7.1%
i 2875
 
7.1%
n 2875
 
7.1%
t 2869
 
7.1%
r 1458
 
3.6%
Other values (19) 13024
32.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40446
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2903
 
7.2%
a 2898
 
7.2%
e 2894
 
7.2%
o 2892
 
7.2%
S 2879
 
7.1%
d 2879
 
7.1%
i 2875
 
7.1%
n 2875
 
7.1%
t 2869
 
7.1%
r 1458
 
3.6%
Other values (19) 13024
32.2%

Exterior1st
Categorical

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
Vinyl Siding
515 
Hard Board
222 
Metal Siding
220 
Wood Siding
206 
Plywood
108 
Other values (10)
189 

Length

Max length17
Median length12
Mean length11.09589
Min length5

Characters and Unicode

Total characters16200
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st rowVinyl Siding
2nd rowMetal Siding
3rd rowVinyl Siding
4th rowWood Siding
5th rowVinyl Siding

Common Values

ValueCountFrequency (%)
Vinyl Siding 515
35.3%
Hard Board 222
15.2%
Metal Siding 220
15.1%
Wood Siding 206
 
14.1%
Plywood 108
 
7.4%
Cement Board 61
 
4.2%
Brick Face 50
 
3.4%
Wood Shingles 26
 
1.8%
Stucco 25
 
1.7%
Asbestos Shingles 20
 
1.4%
Other values (5) 7
 
0.5%

Length

2025-01-27T18:27:20.382248image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
siding 941
33.8%
vinyl 515
18.5%
board 283
 
10.2%
wood 232
 
8.3%
hard 222
 
8.0%
metal 220
 
7.9%
plywood 108
 
3.9%
cement 61
 
2.2%
brick 52
 
1.9%
face 50
 
1.8%
Other values (9) 101
 
3.6%

Most occurring characters

ValueCountFrequency (%)
i 2499
15.4%
d 1787
11.0%
n 1570
9.7%
1325
 
8.2%
o 1017
 
6.3%
S 1016
 
6.3%
g 988
 
6.1%
l 892
 
5.5%
a 777
 
4.8%
y 623
 
3.8%
Other values (22) 3706
22.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16200
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 2499
15.4%
d 1787
11.0%
n 1570
9.7%
1325
 
8.2%
o 1017
 
6.3%
S 1016
 
6.3%
g 988
 
6.1%
l 892
 
5.5%
a 777
 
4.8%
y 623
 
3.8%
Other values (22) 3706
22.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16200
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 2499
15.4%
d 1787
11.0%
n 1570
9.7%
1325
 
8.2%
o 1017
 
6.3%
S 1016
 
6.3%
g 988
 
6.1%
l 892
 
5.5%
a 777
 
4.8%
y 623
 
3.8%
Other values (22) 3706
22.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16200
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 2499
15.4%
d 1787
11.0%
n 1570
9.7%
1325
 
8.2%
o 1017
 
6.3%
S 1016
 
6.3%
g 988
 
6.1%
l 892
 
5.5%
a 777
 
4.8%
y 623
 
3.8%
Other values (22) 3706
22.9%

Exterior2nd
Categorical

Distinct16
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
Vinyl Siding
504 
Metal Siding
214 
Hard Board
207 
Wood Siding
197 
Plywood
142 
Other values (11)
196 

Length

Max length17
Median length16
Mean length10.669863
Min length5

Characters and Unicode

Total characters15578
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowVinyl Siding
2nd rowMetal Siding
3rd rowVinyl Siding
4th rowWd Shng
5th rowVinyl Siding

Common Values

ValueCountFrequency (%)
Vinyl Siding 504
34.5%
Metal Siding 214
14.7%
Hard Board 207
14.2%
Wood Siding 197
 
13.5%
Plywood 142
 
9.7%
CmentBd 60
 
4.1%
Wd Shng 38
 
2.6%
Stucco 26
 
1.8%
Brick Face 25
 
1.7%
Asbestos Shingles 20
 
1.4%
Other values (6) 27
 
1.8%

Length

2025-01-27T18:27:20.939787image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
siding 915
34.1%
vinyl 504
18.8%
metal 214
 
8.0%
hard 207
 
7.7%
board 207
 
7.7%
wood 197
 
7.3%
plywood 142
 
5.3%
cmentbd 60
 
2.2%
wd 38
 
1.4%
shng 38
 
1.4%
Other values (13) 164
 
6.1%

Most occurring characters

ValueCountFrequency (%)
i 2403
15.4%
d 1767
11.3%
n 1563
10.0%
1226
 
7.9%
S 1017
 
6.5%
g 976
 
6.3%
o 957
 
6.1%
l 887
 
5.7%
a 666
 
4.3%
y 646
 
4.1%
Other values (23) 3470
22.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15578
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 2403
15.4%
d 1767
11.3%
n 1563
10.0%
1226
 
7.9%
S 1017
 
6.5%
g 976
 
6.3%
o 957
 
6.1%
l 887
 
5.7%
a 666
 
4.3%
y 646
 
4.1%
Other values (23) 3470
22.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15578
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 2403
15.4%
d 1767
11.3%
n 1563
10.0%
1226
 
7.9%
S 1017
 
6.5%
g 976
 
6.3%
o 957
 
6.1%
l 887
 
5.7%
a 666
 
4.3%
y 646
 
4.1%
Other values (23) 3470
22.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15578
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 2403
15.4%
d 1767
11.3%
n 1563
10.0%
1226
 
7.9%
S 1017
 
6.5%
g 976
 
6.3%
o 957
 
6.1%
l 887
 
5.7%
a 666
 
4.3%
y 646
 
4.1%
Other values (23) 3470
22.3%

MasVnrType
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
nan
872 
Brick Face
445 
Stone
128 
Brick Common
 
15

Length

Max length12
Median length3
Mean length5.4013699
Min length3

Characters and Unicode

Total characters7886
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBrick Face
2nd rownan
3rd rowBrick Face
4th rownan
5th rowBrick Face

Common Values

ValueCountFrequency (%)
nan 872
59.7%
Brick Face 445
30.5%
Stone 128
 
8.8%
Brick Common 15
 
1.0%

Length

2025-01-27T18:27:21.467250image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T18:27:21.905424image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
nan 872
45.4%
brick 460
24.0%
face 445
23.2%
stone 128
 
6.7%
common 15
 
0.8%

Most occurring characters

ValueCountFrequency (%)
n 1887
23.9%
a 1317
16.7%
c 905
11.5%
e 573
 
7.3%
B 460
 
5.8%
r 460
 
5.8%
i 460
 
5.8%
k 460
 
5.8%
460
 
5.8%
F 445
 
5.6%
Other values (5) 459
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7886
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 1887
23.9%
a 1317
16.7%
c 905
11.5%
e 573
 
7.3%
B 460
 
5.8%
r 460
 
5.8%
i 460
 
5.8%
k 460
 
5.8%
460
 
5.8%
F 445
 
5.6%
Other values (5) 459
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7886
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 1887
23.9%
a 1317
16.7%
c 905
11.5%
e 573
 
7.3%
B 460
 
5.8%
r 460
 
5.8%
i 460
 
5.8%
k 460
 
5.8%
460
 
5.8%
F 445
 
5.6%
Other values (5) 459
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7886
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 1887
23.9%
a 1317
16.7%
c 905
11.5%
e 573
 
7.3%
B 460
 
5.8%
r 460
 
5.8%
i 460
 
5.8%
k 460
 
5.8%
460
 
5.8%
F 445
 
5.6%
Other values (5) 459
 
5.8%

MasVnrArea
Real number (ℝ)

Zeros 

Distinct327
Distinct (%)22.5%
Missing8
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean103.68526
Minimum0
Maximum1600
Zeros861
Zeros (%)59.0%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2025-01-27T18:27:22.399463image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3166
95-th percentile456
Maximum1600
Range1600
Interquartile range (IQR)166

Descriptive statistics

Standard deviation181.06621
Coefficient of variation (CV)1.7463061
Kurtosis10.082417
Mean103.68526
Median Absolute Deviation (MAD)0
Skewness2.6690842
Sum150551
Variance32784.971
MonotonicityNot monotonic
2025-01-27T18:27:22.957793image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 861
59.0%
72 8
 
0.5%
108 8
 
0.5%
180 8
 
0.5%
120 7
 
0.5%
16 7
 
0.5%
340 6
 
0.4%
106 6
 
0.4%
80 6
 
0.4%
200 6
 
0.4%
Other values (317) 529
36.2%
(Missing) 8
 
0.5%
ValueCountFrequency (%)
0 861
59.0%
1 2
 
0.1%
11 1
 
0.1%
14 1
 
0.1%
16 7
 
0.5%
18 2
 
0.1%
22 1
 
0.1%
24 1
 
0.1%
27 1
 
0.1%
28 1
 
0.1%
ValueCountFrequency (%)
1600 1
0.1%
1378 1
0.1%
1170 1
0.1%
1129 1
0.1%
1115 1
0.1%
1047 1
0.1%
1031 1
0.1%
975 1
0.1%
922 1
0.1%
921 1
0.1%

ExterQual
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
Average/Typical
906 
Good
488 
Excellent
 
52
Fair
 
14

Length

Max length15
Median length15
Mean length11.00411
Min length4

Characters and Unicode

Total characters16066
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGood
2nd rowAverage/Typical
3rd rowGood
4th rowAverage/Typical
5th rowGood

Common Values

ValueCountFrequency (%)
Average/Typical 906
62.1%
Good 488
33.4%
Excellent 52
 
3.6%
Fair 14
 
1.0%

Length

2025-01-27T18:27:23.455963image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T18:27:24.116041image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
average/typical 906
62.1%
good 488
33.4%
excellent 52
 
3.6%
fair 14
 
1.0%

Most occurring characters

ValueCountFrequency (%)
e 1916
 
11.9%
a 1826
 
11.4%
l 1010
 
6.3%
o 976
 
6.1%
c 958
 
6.0%
i 920
 
5.7%
r 920
 
5.7%
p 906
 
5.6%
v 906
 
5.6%
A 906
 
5.6%
Other values (11) 4822
30.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16066
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1916
 
11.9%
a 1826
 
11.4%
l 1010
 
6.3%
o 976
 
6.1%
c 958
 
6.0%
i 920
 
5.7%
r 920
 
5.7%
p 906
 
5.6%
v 906
 
5.6%
A 906
 
5.6%
Other values (11) 4822
30.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16066
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1916
 
11.9%
a 1826
 
11.4%
l 1010
 
6.3%
o 976
 
6.1%
c 958
 
6.0%
i 920
 
5.7%
r 920
 
5.7%
p 906
 
5.6%
v 906
 
5.6%
A 906
 
5.6%
Other values (11) 4822
30.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16066
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1916
 
11.9%
a 1826
 
11.4%
l 1010
 
6.3%
o 976
 
6.1%
c 958
 
6.0%
i 920
 
5.7%
r 920
 
5.7%
p 906
 
5.6%
v 906
 
5.6%
A 906
 
5.6%
Other values (11) 4822
30.0%

ExterCond
Categorical

Imbalance 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
Average/Typical
1282 
Good
146 
Fair
 
28
Excellent
 
3
Poor
 
1

Length

Max length15
Median length15
Mean length13.669178
Min length4

Characters and Unicode

Total characters19957
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowAverage/Typical
2nd rowAverage/Typical
3rd rowAverage/Typical
4th rowAverage/Typical
5th rowAverage/Typical

Common Values

ValueCountFrequency (%)
Average/Typical 1282
87.8%
Good 146
 
10.0%
Fair 28
 
1.9%
Excellent 3
 
0.2%
Poor 1
 
0.1%

Length

2025-01-27T18:27:24.482104image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T18:27:24.831232image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
average/typical 1282
87.8%
good 146
 
10.0%
fair 28
 
1.9%
excellent 3
 
0.2%
poor 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
a 2592
13.0%
e 2570
12.9%
r 1311
 
6.6%
i 1310
 
6.6%
l 1288
 
6.5%
c 1285
 
6.4%
p 1282
 
6.4%
v 1282
 
6.4%
A 1282
 
6.4%
y 1282
 
6.4%
Other values (12) 4473
22.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19957
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2592
13.0%
e 2570
12.9%
r 1311
 
6.6%
i 1310
 
6.6%
l 1288
 
6.5%
c 1285
 
6.4%
p 1282
 
6.4%
v 1282
 
6.4%
A 1282
 
6.4%
y 1282
 
6.4%
Other values (12) 4473
22.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19957
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2592
13.0%
e 2570
12.9%
r 1311
 
6.6%
i 1310
 
6.6%
l 1288
 
6.5%
c 1285
 
6.4%
p 1282
 
6.4%
v 1282
 
6.4%
A 1282
 
6.4%
y 1282
 
6.4%
Other values (12) 4473
22.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19957
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2592
13.0%
e 2570
12.9%
r 1311
 
6.6%
i 1310
 
6.6%
l 1288
 
6.5%
c 1285
 
6.4%
p 1282
 
6.4%
v 1282
 
6.4%
A 1282
 
6.4%
y 1282
 
6.4%
Other values (12) 4473
22.4%

Foundation
Categorical

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
Poured Contrete
647 
Cinder Block
634 
Brick & Tile
146 
Slab
 
24
Stone
 
6

Length

Max length15
Median length12
Mean length13.15274
Min length4

Characters and Unicode

Total characters19203
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPoured Contrete
2nd rowCinder Block
3rd rowPoured Contrete
4th rowBrick & Tile
5th rowPoured Contrete

Common Values

ValueCountFrequency (%)
Poured Contrete 647
44.3%
Cinder Block 634
43.4%
Brick & Tile 146
 
10.0%
Slab 24
 
1.6%
Stone 6
 
0.4%
Wood 3
 
0.2%

Length

2025-01-27T18:27:25.210359image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T18:27:25.521115image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
poured 647
21.3%
contrete 647
21.3%
cinder 634
20.9%
block 634
20.9%
brick 146
 
4.8%
146
 
4.8%
tile 146
 
4.8%
slab 24
 
0.8%
stone 6
 
0.2%
wood 3
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 2727
14.2%
r 2074
10.8%
o 1940
10.1%
1573
 
8.2%
t 1300
 
6.8%
n 1287
 
6.7%
d 1284
 
6.7%
C 1281
 
6.7%
i 926
 
4.8%
l 804
 
4.2%
Other values (11) 4007
20.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19203
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2727
14.2%
r 2074
10.8%
o 1940
10.1%
1573
 
8.2%
t 1300
 
6.8%
n 1287
 
6.7%
d 1284
 
6.7%
C 1281
 
6.7%
i 926
 
4.8%
l 804
 
4.2%
Other values (11) 4007
20.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19203
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2727
14.2%
r 2074
10.8%
o 1940
10.1%
1573
 
8.2%
t 1300
 
6.8%
n 1287
 
6.7%
d 1284
 
6.7%
C 1281
 
6.7%
i 926
 
4.8%
l 804
 
4.2%
Other values (11) 4007
20.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19203
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2727
14.2%
r 2074
10.8%
o 1940
10.1%
1573
 
8.2%
t 1300
 
6.8%
n 1287
 
6.7%
d 1284
 
6.7%
C 1281
 
6.7%
i 926
 
4.8%
l 804
 
4.2%
Other values (11) 4007
20.9%

BsmtQual
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
Typical (80-89 inches)
649 
Good (90-99 inches)
618 
Excellent (100+ inches)
121 
nan
 
37
Fair (70-79 inches)
 
35

Length

Max length23
Median length22
Mean length20.259589
Min length3

Characters and Unicode

Total characters29579
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGood (90-99 inches)
2nd rowGood (90-99 inches)
3rd rowGood (90-99 inches)
4th rowTypical (80-89 inches)
5th rowGood (90-99 inches)

Common Values

ValueCountFrequency (%)
Typical (80-89 inches) 649
44.5%
Good (90-99 inches) 618
42.3%
Excellent (100+ inches) 121
 
8.3%
nan 37
 
2.5%
Fair (70-79 inches) 35
 
2.4%

Length

2025-01-27T18:27:25.838017image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T18:27:26.305969image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
inches 1423
33.0%
typical 649
15.1%
80-89 649
15.1%
good 618
14.4%
90-99 618
14.4%
excellent 121
 
2.8%
100 121
 
2.8%
nan 37
 
0.9%
fair 35
 
0.8%
70-79 35
 
0.8%

Most occurring characters

ValueCountFrequency (%)
2846
 
9.6%
9 2538
 
8.6%
c 2193
 
7.4%
i 2107
 
7.1%
e 1665
 
5.6%
n 1618
 
5.5%
0 1544
 
5.2%
h 1423
 
4.8%
s 1423
 
4.8%
( 1423
 
4.8%
Other values (19) 10799
36.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 29579
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2846
 
9.6%
9 2538
 
8.6%
c 2193
 
7.4%
i 2107
 
7.1%
e 1665
 
5.6%
n 1618
 
5.5%
0 1544
 
5.2%
h 1423
 
4.8%
s 1423
 
4.8%
( 1423
 
4.8%
Other values (19) 10799
36.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 29579
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2846
 
9.6%
9 2538
 
8.6%
c 2193
 
7.4%
i 2107
 
7.1%
e 1665
 
5.6%
n 1618
 
5.5%
0 1544
 
5.2%
h 1423
 
4.8%
s 1423
 
4.8%
( 1423
 
4.8%
Other values (19) 10799
36.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 29579
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2846
 
9.6%
9 2538
 
8.6%
c 2193
 
7.4%
i 2107
 
7.1%
e 1665
 
5.6%
n 1618
 
5.5%
0 1544
 
5.2%
h 1423
 
4.8%
s 1423
 
4.8%
( 1423
 
4.8%
Other values (19) 10799
36.5%

BsmtCond
Categorical

Imbalance 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
Typical - slight dampness allowed
1311 
Good
 
65
Fair - dampness or some cracking or settling
 
45
nan
 
37
Poor - Severe cracking, settling, or wetness
 
2

Length

Max length44
Median length33
Mean length31.30274
Min length3

Characters and Unicode

Total characters45702
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTypical - slight dampness allowed
2nd rowTypical - slight dampness allowed
3rd rowTypical - slight dampness allowed
4th rowGood
5th rowTypical - slight dampness allowed

Common Values

ValueCountFrequency (%)
Typical - slight dampness allowed 1311
89.8%
Good 65
 
4.5%
Fair - dampness or some cracking or settling 45
 
3.1%
nan 37
 
2.5%
Poor - Severe cracking, settling, or wetness 2
 
0.1%

Length

2025-01-27T18:27:26.685189image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T18:27:27.055539image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1358
19.3%
dampness 1356
19.3%
typical 1311
18.6%
slight 1311
18.6%
allowed 1311
18.6%
or 92
 
1.3%
good 65
 
0.9%
cracking 47
 
0.7%
settling 47
 
0.7%
fair 45
 
0.6%
Other values (5) 88
 
1.3%

Most occurring characters

ValueCountFrequency (%)
5571
12.2%
l 5291
11.6%
s 4119
 
9.0%
a 4107
 
9.0%
e 2769
 
6.1%
i 2761
 
6.0%
d 2732
 
6.0%
p 2667
 
5.8%
o 1582
 
3.5%
n 1526
 
3.3%
Other values (17) 12577
27.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45702
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5571
12.2%
l 5291
11.6%
s 4119
 
9.0%
a 4107
 
9.0%
e 2769
 
6.1%
i 2761
 
6.0%
d 2732
 
6.0%
p 2667
 
5.8%
o 1582
 
3.5%
n 1526
 
3.3%
Other values (17) 12577
27.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45702
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5571
12.2%
l 5291
11.6%
s 4119
 
9.0%
a 4107
 
9.0%
e 2769
 
6.1%
i 2761
 
6.0%
d 2732
 
6.0%
p 2667
 
5.8%
o 1582
 
3.5%
n 1526
 
3.3%
Other values (17) 12577
27.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45702
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5571
12.2%
l 5291
11.6%
s 4119
 
9.0%
a 4107
 
9.0%
e 2769
 
6.1%
i 2761
 
6.0%
d 2732
 
6.0%
p 2667
 
5.8%
o 1582
 
3.5%
n 1526
 
3.3%
Other values (17) 12577
27.5%

BsmtExposure
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
No Exposure
953 
Average Exposure (split levels or foyers typically score average or above)
221 
Good Exposure
134 
Mimimum Exposure
114 
nan
 
38

Length

Max length74
Median length11
Mean length20.902055
Min length3

Characters and Unicode

Total characters30517
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo Exposure
2nd rowGood Exposure
3rd rowMimimum Exposure
4th rowNo Exposure
5th rowAverage Exposure (split levels or foyers typically score average or above)

Common Values

ValueCountFrequency (%)
No Exposure 953
65.3%
Average Exposure (split levels or foyers typically score average or above) 221
 
15.1%
Good Exposure 134
 
9.2%
Mimimum Exposure 114
 
7.8%
nan 38
 
2.6%

Length

2025-01-27T18:27:27.636058image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T18:27:28.190346image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
exposure 1422
29.2%
no 953
19.6%
average 442
 
9.1%
or 442
 
9.1%
split 221
 
4.5%
levels 221
 
4.5%
foyers 221
 
4.5%
typically 221
 
4.5%
score 221
 
4.5%
above 221
 
4.5%
Other values (3) 286
 
5.9%

Most occurring characters

ValueCountFrequency (%)
o 3748
12.3%
3411
11.2%
e 3411
11.2%
r 2748
 
9.0%
s 2306
 
7.6%
p 1864
 
6.1%
u 1536
 
5.0%
E 1422
 
4.7%
x 1422
 
4.7%
a 1143
 
3.7%
Other values (18) 7506
24.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30517
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 3748
12.3%
3411
11.2%
e 3411
11.2%
r 2748
 
9.0%
s 2306
 
7.6%
p 1864
 
6.1%
u 1536
 
5.0%
E 1422
 
4.7%
x 1422
 
4.7%
a 1143
 
3.7%
Other values (18) 7506
24.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30517
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 3748
12.3%
3411
11.2%
e 3411
11.2%
r 2748
 
9.0%
s 2306
 
7.6%
p 1864
 
6.1%
u 1536
 
5.0%
E 1422
 
4.7%
x 1422
 
4.7%
a 1143
 
3.7%
Other values (18) 7506
24.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30517
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 3748
12.3%
3411
11.2%
e 3411
11.2%
r 2748
 
9.0%
s 2306
 
7.6%
p 1864
 
6.1%
u 1536
 
5.0%
E 1422
 
4.7%
x 1422
 
4.7%
a 1143
 
3.7%
Other values (18) 7506
24.6%

BsmtFinType1
Categorical

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
Unfinshed
430 
Good Living Quarters
418 
Average Living Quarters
220 
Below Average Living Quarters
148 
Average Rec Room
133 
Other values (2)
111 

Length

Max length29
Median length23
Mean length16.873288
Min length3

Characters and Unicode

Total characters24635
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGood Living Quarters
2nd rowAverage Living Quarters
3rd rowGood Living Quarters
4th rowAverage Living Quarters
5th rowGood Living Quarters

Common Values

ValueCountFrequency (%)
Unfinshed 430
29.5%
Good Living Quarters 418
28.6%
Average Living Quarters 220
15.1%
Below Average Living Quarters 148
 
10.1%
Average Rec Room 133
 
9.1%
Low Quality 74
 
5.1%
nan 37
 
2.5%

Length

2025-01-27T18:27:28.758007image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T18:27:29.138297image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
living 786
22.3%
quarters 786
22.3%
average 501
14.2%
unfinshed 430
12.2%
good 418
11.9%
below 148
 
4.2%
rec 133
 
3.8%
room 133
 
3.8%
low 74
 
2.1%
quality 74
 
2.1%

Most occurring characters

ValueCountFrequency (%)
e 2499
 
10.1%
i 2076
 
8.4%
r 2073
 
8.4%
2060
 
8.4%
n 1720
 
7.0%
a 1398
 
5.7%
o 1324
 
5.4%
g 1287
 
5.2%
v 1287
 
5.2%
s 1216
 
4.9%
Other values (17) 7695
31.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24635
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2499
 
10.1%
i 2076
 
8.4%
r 2073
 
8.4%
2060
 
8.4%
n 1720
 
7.0%
a 1398
 
5.7%
o 1324
 
5.4%
g 1287
 
5.2%
v 1287
 
5.2%
s 1216
 
4.9%
Other values (17) 7695
31.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24635
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2499
 
10.1%
i 2076
 
8.4%
r 2073
 
8.4%
2060
 
8.4%
n 1720
 
7.0%
a 1398
 
5.7%
o 1324
 
5.4%
g 1287
 
5.2%
v 1287
 
5.2%
s 1216
 
4.9%
Other values (17) 7695
31.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24635
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2499
 
10.1%
i 2076
 
8.4%
r 2073
 
8.4%
2060
 
8.4%
n 1720
 
7.0%
a 1398
 
5.7%
o 1324
 
5.4%
g 1287
 
5.2%
v 1287
 
5.2%
s 1216
 
4.9%
Other values (17) 7695
31.2%

BsmtFinSF1
Real number (ℝ)

Zeros 

Distinct637
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean443.63973
Minimum0
Maximum5644
Zeros467
Zeros (%)32.0%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2025-01-27T18:27:29.556302image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median383.5
Q3712.25
95-th percentile1274
Maximum5644
Range5644
Interquartile range (IQR)712.25

Descriptive statistics

Standard deviation456.09809
Coefficient of variation (CV)1.0280822
Kurtosis11.118236
Mean443.63973
Median Absolute Deviation (MAD)383.5
Skewness1.6855031
Sum647714
Variance208025.47
MonotonicityNot monotonic
2025-01-27T18:27:30.007055image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 467
32.0%
24 12
 
0.8%
16 9
 
0.6%
686 5
 
0.3%
662 5
 
0.3%
20 5
 
0.3%
936 5
 
0.3%
616 5
 
0.3%
560 4
 
0.3%
553 4
 
0.3%
Other values (627) 939
64.3%
ValueCountFrequency (%)
0 467
32.0%
2 1
 
0.1%
16 9
 
0.6%
20 5
 
0.3%
24 12
 
0.8%
25 1
 
0.1%
27 1
 
0.1%
28 3
 
0.2%
33 1
 
0.1%
35 1
 
0.1%
ValueCountFrequency (%)
5644 1
0.1%
2260 1
0.1%
2188 1
0.1%
2096 1
0.1%
1904 1
0.1%
1880 1
0.1%
1810 1
0.1%
1767 1
0.1%
1721 1
0.1%
1696 1
0.1%

BsmtFinType2
Categorical

Imbalance 

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
Unfinshed
1256 
Average Rec Room
 
54
Low Quality
 
46
nan
 
38
Below Average Living Quarters
 
33
Other values (2)
 
33

Length

Max length29
Median length9
Mean length9.9054795
Min length3

Characters and Unicode

Total characters14462
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnfinshed
2nd rowUnfinshed
3rd rowUnfinshed
4th rowUnfinshed
5th rowUnfinshed

Common Values

ValueCountFrequency (%)
Unfinshed 1256
86.0%
Average Rec Room 54
 
3.7%
Low Quality 46
 
3.2%
nan 38
 
2.6%
Below Average Living Quarters 33
 
2.3%
Average Living Quarters 19
 
1.3%
Good Living Quarters 14
 
1.0%

Length

2025-01-27T18:27:30.395992image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T18:27:30.938839image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
unfinshed 1256
70.6%
average 106
 
6.0%
living 66
 
3.7%
quarters 66
 
3.7%
rec 54
 
3.0%
room 54
 
3.0%
low 46
 
2.6%
quality 46
 
2.6%
nan 38
 
2.1%
below 33
 
1.9%

Most occurring characters

ValueCountFrequency (%)
n 2654
18.4%
e 1621
11.2%
i 1434
9.9%
s 1322
9.1%
d 1270
8.8%
U 1256
8.7%
f 1256
8.7%
h 1256
8.7%
319
 
2.2%
a 256
 
1.8%
Other values (17) 1818
12.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14462
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 2654
18.4%
e 1621
11.2%
i 1434
9.9%
s 1322
9.1%
d 1270
8.8%
U 1256
8.7%
f 1256
8.7%
h 1256
8.7%
319
 
2.2%
a 256
 
1.8%
Other values (17) 1818
12.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14462
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 2654
18.4%
e 1621
11.2%
i 1434
9.9%
s 1322
9.1%
d 1270
8.8%
U 1256
8.7%
f 1256
8.7%
h 1256
8.7%
319
 
2.2%
a 256
 
1.8%
Other values (17) 1818
12.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14462
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 2654
18.4%
e 1621
11.2%
i 1434
9.9%
s 1322
9.1%
d 1270
8.8%
U 1256
8.7%
f 1256
8.7%
h 1256
8.7%
319
 
2.2%
a 256
 
1.8%
Other values (17) 1818
12.6%

BsmtFinSF2
Real number (ℝ)

Zeros 

Distinct144
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.549315
Minimum0
Maximum1474
Zeros1293
Zeros (%)88.6%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2025-01-27T18:27:31.456371image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile396.2
Maximum1474
Range1474
Interquartile range (IQR)0

Descriptive statistics

Standard deviation161.31927
Coefficient of variation (CV)3.4655563
Kurtosis20.113338
Mean46.549315
Median Absolute Deviation (MAD)0
Skewness4.2552611
Sum67962
Variance26023.908
MonotonicityNot monotonic
2025-01-27T18:27:31.999238image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1293
88.6%
180 5
 
0.3%
374 3
 
0.2%
551 2
 
0.1%
147 2
 
0.1%
294 2
 
0.1%
391 2
 
0.1%
539 2
 
0.1%
96 2
 
0.1%
480 2
 
0.1%
Other values (134) 145
 
9.9%
ValueCountFrequency (%)
0 1293
88.6%
28 1
 
0.1%
32 1
 
0.1%
35 1
 
0.1%
40 1
 
0.1%
41 2
 
0.1%
64 2
 
0.1%
68 1
 
0.1%
80 1
 
0.1%
81 1
 
0.1%
ValueCountFrequency (%)
1474 1
0.1%
1127 1
0.1%
1120 1
0.1%
1085 1
0.1%
1080 1
0.1%
1063 1
0.1%
1061 1
0.1%
1057 1
0.1%
1031 1
0.1%
1029 1
0.1%

BsmtUnfSF
Real number (ℝ)

Zeros 

Distinct780
Distinct (%)53.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean567.24041
Minimum0
Maximum2336
Zeros118
Zeros (%)8.1%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2025-01-27T18:27:32.577895image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1223
median477.5
Q3808
95-th percentile1468
Maximum2336
Range2336
Interquartile range (IQR)585

Descriptive statistics

Standard deviation441.86696
Coefficient of variation (CV)0.77897651
Kurtosis0.47499399
Mean567.24041
Median Absolute Deviation (MAD)288
Skewness0.92026845
Sum828171
Variance195246.41
MonotonicityNot monotonic
2025-01-27T18:27:33.095500image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 118
 
8.1%
728 9
 
0.6%
384 8
 
0.5%
600 7
 
0.5%
300 7
 
0.5%
572 7
 
0.5%
270 6
 
0.4%
625 6
 
0.4%
672 6
 
0.4%
440 6
 
0.4%
Other values (770) 1280
87.7%
ValueCountFrequency (%)
0 118
8.1%
14 1
 
0.1%
15 1
 
0.1%
23 2
 
0.1%
26 1
 
0.1%
29 1
 
0.1%
30 1
 
0.1%
32 2
 
0.1%
35 1
 
0.1%
36 4
 
0.3%
ValueCountFrequency (%)
2336 1
0.1%
2153 1
0.1%
2121 1
0.1%
2046 1
0.1%
2042 1
0.1%
2002 1
0.1%
1969 1
0.1%
1935 1
0.1%
1926 1
0.1%
1907 1
0.1%

TotalBsmtSF
Real number (ℝ)

Zeros 

Distinct721
Distinct (%)49.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1057.4295
Minimum0
Maximum6110
Zeros37
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2025-01-27T18:27:33.555373image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile519.3
Q1795.75
median991.5
Q31298.25
95-th percentile1753
Maximum6110
Range6110
Interquartile range (IQR)502.5

Descriptive statistics

Standard deviation438.70532
Coefficient of variation (CV)0.41487905
Kurtosis13.250483
Mean1057.4295
Median Absolute Deviation (MAD)234.5
Skewness1.5242545
Sum1543847
Variance192462.36
MonotonicityNot monotonic
2025-01-27T18:27:33.918081image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 37
 
2.5%
864 35
 
2.4%
672 17
 
1.2%
912 15
 
1.0%
1040 14
 
1.0%
816 13
 
0.9%
768 12
 
0.8%
728 12
 
0.8%
894 11
 
0.8%
780 11
 
0.8%
Other values (711) 1283
87.9%
ValueCountFrequency (%)
0 37
2.5%
105 1
 
0.1%
190 1
 
0.1%
264 3
 
0.2%
270 1
 
0.1%
290 1
 
0.1%
319 1
 
0.1%
360 1
 
0.1%
372 1
 
0.1%
384 7
 
0.5%
ValueCountFrequency (%)
6110 1
0.1%
3206 1
0.1%
3200 1
0.1%
3138 1
0.1%
3094 1
0.1%
2633 1
0.1%
2524 1
0.1%
2444 1
0.1%
2396 1
0.1%
2392 1
0.1%

Heating
Categorical

Imbalance 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
Gas forced warm air furnace
1428 
Gas hot water or steam heat
 
18
Gravity furnace
 
7
Wall furnace
 
4
Hot water or steam heat other than gas
 
2

Length

Max length38
Median length27
Mean length26.906849
Min length12

Characters and Unicode

Total characters39284
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowGas forced warm air furnace
2nd rowGas forced warm air furnace
3rd rowGas forced warm air furnace
4th rowGas forced warm air furnace
5th rowGas forced warm air furnace

Common Values

ValueCountFrequency (%)
Gas forced warm air furnace 1428
97.8%
Gas hot water or steam heat 18
 
1.2%
Gravity furnace 7
 
0.5%
Wall furnace 4
 
0.3%
Hot water or steam heat other than gas 2
 
0.1%
Floor Furnace 1
 
0.1%

Length

2025-01-27T18:27:34.268199image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T18:27:34.605958image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
gas 1448
19.9%
furnace 1440
19.8%
forced 1428
19.6%
warm 1428
19.6%
air 1428
19.6%
hot 20
 
0.3%
water 20
 
0.3%
or 20
 
0.3%
steam 20
 
0.3%
heat 20
 
0.3%
Other values (5) 16
 
0.2%

Most occurring characters

ValueCountFrequency (%)
5828
14.8%
a 5817
14.8%
r 5774
14.7%
e 2930
 
7.5%
c 2868
 
7.3%
f 2867
 
7.3%
o 1472
 
3.7%
s 1468
 
3.7%
G 1453
 
3.7%
w 1448
 
3.7%
Other values (14) 7359
18.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 39284
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5828
14.8%
a 5817
14.8%
r 5774
14.7%
e 2930
 
7.5%
c 2868
 
7.3%
f 2867
 
7.3%
o 1472
 
3.7%
s 1468
 
3.7%
G 1453
 
3.7%
w 1448
 
3.7%
Other values (14) 7359
18.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 39284
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5828
14.8%
a 5817
14.8%
r 5774
14.7%
e 2930
 
7.5%
c 2868
 
7.3%
f 2867
 
7.3%
o 1472
 
3.7%
s 1468
 
3.7%
G 1453
 
3.7%
w 1448
 
3.7%
Other values (14) 7359
18.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 39284
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5828
14.8%
a 5817
14.8%
r 5774
14.7%
e 2930
 
7.5%
c 2868
 
7.3%
f 2867
 
7.3%
o 1472
 
3.7%
s 1468
 
3.7%
G 1453
 
3.7%
w 1448
 
3.7%
Other values (14) 7359
18.7%

HeatingQC
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
Excellent
741 
Average/Typical
428 
Good
241 
Fair
 
49
Poor
 
1

Length

Max length15
Median length9
Mean length9.7623288
Min length4

Characters and Unicode

Total characters14253
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowExcellent
2nd rowExcellent
3rd rowExcellent
4th rowGood
5th rowExcellent

Common Values

ValueCountFrequency (%)
Excellent 741
50.8%
Average/Typical 428
29.3%
Good 241
 
16.5%
Fair 49
 
3.4%
Poor 1
 
0.1%

Length

2025-01-27T18:27:34.956392image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T18:27:35.293738image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
excellent 741
50.8%
average/typical 428
29.3%
good 241
 
16.5%
fair 49
 
3.4%
poor 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 2338
16.4%
l 1910
13.4%
c 1169
 
8.2%
a 905
 
6.3%
E 741
 
5.2%
n 741
 
5.2%
t 741
 
5.2%
x 741
 
5.2%
o 484
 
3.4%
r 478
 
3.4%
Other values (12) 4005
28.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14253
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2338
16.4%
l 1910
13.4%
c 1169
 
8.2%
a 905
 
6.3%
E 741
 
5.2%
n 741
 
5.2%
t 741
 
5.2%
x 741
 
5.2%
o 484
 
3.4%
r 478
 
3.4%
Other values (12) 4005
28.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14253
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2338
16.4%
l 1910
13.4%
c 1169
 
8.2%
a 905
 
6.3%
E 741
 
5.2%
n 741
 
5.2%
t 741
 
5.2%
x 741
 
5.2%
o 484
 
3.4%
r 478
 
3.4%
Other values (12) 4005
28.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14253
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2338
16.4%
l 1910
13.4%
c 1169
 
8.2%
a 905
 
6.3%
E 741
 
5.2%
n 741
 
5.2%
t 741
 
5.2%
x 741
 
5.2%
o 484
 
3.4%
r 478
 
3.4%
Other values (12) 4005
28.1%

CentralAir
Boolean

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
True
1365 
False
 
95
ValueCountFrequency (%)
True 1365
93.5%
False 95
 
6.5%
2025-01-27T18:27:35.597926image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Electrical
Categorical

Imbalance 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
Standard Circuit Breakers & Romex
1334 
Fuse Box over 60 AMP and all Romex wiring (Average)
 
94
60 AMP Fuse Box and mostly Romex wiring (Fair)
 
27
60 AMP Fuse Box and mostly knob & tube wiring (poor)
 
3
Mixed
 
1

Length

Max length52
Median length33
Mean length34.39863
Min length3

Characters and Unicode

Total characters50222
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowStandard Circuit Breakers & Romex
2nd rowStandard Circuit Breakers & Romex
3rd rowStandard Circuit Breakers & Romex
4th rowStandard Circuit Breakers & Romex
5th rowStandard Circuit Breakers & Romex

Common Values

ValueCountFrequency (%)
Standard Circuit Breakers & Romex 1334
91.4%
Fuse Box over 60 AMP and all Romex wiring (Average) 94
 
6.4%
60 AMP Fuse Box and mostly Romex wiring (Fair) 27
 
1.8%
60 AMP Fuse Box and mostly knob & tube wiring (poor) 3
 
0.2%
Mixed 1
 
0.1%
nan 1
 
0.1%

Length

2025-01-27T18:27:35.886028image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T18:27:36.288207image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
romex 1455
18.4%
1337
16.9%
standard 1334
16.9%
breakers 1334
16.9%
circuit 1334
16.9%
60 124
 
1.6%
wiring 124
 
1.6%
amp 124
 
1.6%
and 124
 
1.6%
box 124
 
1.6%
Other values (11) 474
 
6.0%

Most occurring characters

ValueCountFrequency (%)
6428
12.8%
r 5678
 
11.3%
e 4533
 
9.0%
a 4342
 
8.6%
i 2944
 
5.9%
d 2793
 
5.6%
t 2701
 
5.4%
o 1712
 
3.4%
n 1587
 
3.2%
x 1580
 
3.1%
Other values (25) 15924
31.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 50222
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6428
12.8%
r 5678
 
11.3%
e 4533
 
9.0%
a 4342
 
8.6%
i 2944
 
5.9%
d 2793
 
5.6%
t 2701
 
5.4%
o 1712
 
3.4%
n 1587
 
3.2%
x 1580
 
3.1%
Other values (25) 15924
31.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 50222
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6428
12.8%
r 5678
 
11.3%
e 4533
 
9.0%
a 4342
 
8.6%
i 2944
 
5.9%
d 2793
 
5.6%
t 2701
 
5.4%
o 1712
 
3.4%
n 1587
 
3.2%
x 1580
 
3.1%
Other values (25) 15924
31.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 50222
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6428
12.8%
r 5678
 
11.3%
e 4533
 
9.0%
a 4342
 
8.6%
i 2944
 
5.9%
d 2793
 
5.6%
t 2701
 
5.4%
o 1712
 
3.4%
n 1587
 
3.2%
x 1580
 
3.1%
Other values (25) 15924
31.7%

1stFlrSF
Real number (ℝ)

Distinct753
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1162.6267
Minimum334
Maximum4692
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2025-01-27T18:27:36.681585image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile672.95
Q1882
median1087
Q31391.25
95-th percentile1831.25
Maximum4692
Range4358
Interquartile range (IQR)509.25

Descriptive statistics

Standard deviation386.58774
Coefficient of variation (CV)0.33251235
Kurtosis5.7458415
Mean1162.6267
Median Absolute Deviation (MAD)234.5
Skewness1.3767566
Sum1697435
Variance149450.08
MonotonicityNot monotonic
2025-01-27T18:27:37.068747image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
864 25
 
1.7%
1040 16
 
1.1%
912 14
 
1.0%
894 12
 
0.8%
848 12
 
0.8%
672 11
 
0.8%
630 9
 
0.6%
816 9
 
0.6%
483 7
 
0.5%
960 7
 
0.5%
Other values (743) 1338
91.6%
ValueCountFrequency (%)
334 1
 
0.1%
372 1
 
0.1%
438 1
 
0.1%
480 1
 
0.1%
483 7
0.5%
495 1
 
0.1%
520 5
0.3%
525 1
 
0.1%
526 1
 
0.1%
536 1
 
0.1%
ValueCountFrequency (%)
4692 1
0.1%
3228 1
0.1%
3138 1
0.1%
2898 1
0.1%
2633 1
0.1%
2524 1
0.1%
2515 1
0.1%
2444 1
0.1%
2411 1
0.1%
2402 1
0.1%

2ndFlrSF
Real number (ℝ)

Zeros 

Distinct417
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean346.99247
Minimum0
Maximum2065
Zeros829
Zeros (%)56.8%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2025-01-27T18:27:37.365961image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3728
95-th percentile1141.05
Maximum2065
Range2065
Interquartile range (IQR)728

Descriptive statistics

Standard deviation436.52844
Coefficient of variation (CV)1.2580343
Kurtosis-0.55346356
Mean346.99247
Median Absolute Deviation (MAD)0
Skewness0.81302982
Sum506609
Variance190557.08
MonotonicityNot monotonic
2025-01-27T18:27:37.669328image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 829
56.8%
728 10
 
0.7%
504 9
 
0.6%
546 8
 
0.5%
672 8
 
0.5%
600 7
 
0.5%
720 7
 
0.5%
896 6
 
0.4%
862 5
 
0.3%
780 5
 
0.3%
Other values (407) 566
38.8%
ValueCountFrequency (%)
0 829
56.8%
110 1
 
0.1%
167 1
 
0.1%
192 1
 
0.1%
208 1
 
0.1%
213 1
 
0.1%
220 1
 
0.1%
224 1
 
0.1%
240 2
 
0.1%
252 2
 
0.1%
ValueCountFrequency (%)
2065 1
0.1%
1872 1
0.1%
1818 1
0.1%
1796 1
0.1%
1611 1
0.1%
1589 1
0.1%
1540 1
0.1%
1538 1
0.1%
1523 1
0.1%
1519 1
0.1%

LowQualFinSF
Real number (ℝ)

Zeros 

Distinct24
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8445205
Minimum0
Maximum572
Zeros1434
Zeros (%)98.2%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2025-01-27T18:27:38.008004image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum572
Range572
Interquartile range (IQR)0

Descriptive statistics

Standard deviation48.623081
Coefficient of variation (CV)8.3194303
Kurtosis83.234817
Mean5.8445205
Median Absolute Deviation (MAD)0
Skewness9.0113413
Sum8533
Variance2364.204
MonotonicityNot monotonic
2025-01-27T18:27:38.321180image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 1434
98.2%
80 3
 
0.2%
360 2
 
0.1%
205 1
 
0.1%
479 1
 
0.1%
397 1
 
0.1%
514 1
 
0.1%
120 1
 
0.1%
481 1
 
0.1%
232 1
 
0.1%
Other values (14) 14
 
1.0%
ValueCountFrequency (%)
0 1434
98.2%
53 1
 
0.1%
80 3
 
0.2%
120 1
 
0.1%
144 1
 
0.1%
156 1
 
0.1%
205 1
 
0.1%
232 1
 
0.1%
234 1
 
0.1%
360 2
 
0.1%
ValueCountFrequency (%)
572 1
0.1%
528 1
0.1%
515 1
0.1%
514 1
0.1%
513 1
0.1%
481 1
0.1%
479 1
0.1%
473 1
0.1%
420 1
0.1%
397 1
0.1%

GrLivArea
Real number (ℝ)

Distinct861
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1515.4637
Minimum334
Maximum5642
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2025-01-27T18:27:38.981719image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile848
Q11129.5
median1464
Q31776.75
95-th percentile2466.1
Maximum5642
Range5308
Interquartile range (IQR)647.25

Descriptive statistics

Standard deviation525.48038
Coefficient of variation (CV)0.34674561
Kurtosis4.8951206
Mean1515.4637
Median Absolute Deviation (MAD)326
Skewness1.3665604
Sum2212577
Variance276129.63
MonotonicityNot monotonic
2025-01-27T18:27:39.416606image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
864 22
 
1.5%
1040 14
 
1.0%
894 11
 
0.8%
1456 10
 
0.7%
848 10
 
0.7%
1200 9
 
0.6%
912 9
 
0.6%
816 8
 
0.5%
1092 8
 
0.5%
1728 7
 
0.5%
Other values (851) 1352
92.6%
ValueCountFrequency (%)
334 1
 
0.1%
438 1
 
0.1%
480 1
 
0.1%
520 1
 
0.1%
605 1
 
0.1%
616 1
 
0.1%
630 6
0.4%
672 2
 
0.1%
691 1
 
0.1%
693 1
 
0.1%
ValueCountFrequency (%)
5642 1
0.1%
4676 1
0.1%
4476 1
0.1%
4316 1
0.1%
3627 1
0.1%
3608 1
0.1%
3493 1
0.1%
3447 1
0.1%
3395 1
0.1%
3279 1
0.1%

BsmtFullBath
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
0
856 
1
588 
2
 
15
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 856
58.6%
1 588
40.3%
2 15
 
1.0%
3 1
 
0.1%

Length

2025-01-27T18:27:39.880282image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T18:27:40.300566image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 856
58.6%
1 588
40.3%
2 15
 
1.0%
3 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 856
58.6%
1 588
40.3%
2 15
 
1.0%
3 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 856
58.6%
1 588
40.3%
2 15
 
1.0%
3 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 856
58.6%
1 588
40.3%
2 15
 
1.0%
3 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 856
58.6%
1 588
40.3%
2 15
 
1.0%
3 1
 
0.1%

BsmtHalfBath
Categorical

Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
0
1378 
1
 
80
2
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1378
94.4%
1 80
 
5.5%
2 2
 
0.1%

Length

2025-01-27T18:27:40.711225image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T18:27:41.007272image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 1378
94.4%
1 80
 
5.5%
2 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1378
94.4%
1 80
 
5.5%
2 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1378
94.4%
1 80
 
5.5%
2 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1378
94.4%
1 80
 
5.5%
2 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1378
94.4%
1 80
 
5.5%
2 2
 
0.1%

FullBath
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
2
768 
1
650 
3
 
33
0
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 768
52.6%
1 650
44.5%
3 33
 
2.3%
0 9
 
0.6%

Length

2025-01-27T18:27:41.257319image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T18:27:41.537381image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
2 768
52.6%
1 650
44.5%
3 33
 
2.3%
0 9
 
0.6%

Most occurring characters

ValueCountFrequency (%)
2 768
52.6%
1 650
44.5%
3 33
 
2.3%
0 9
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 768
52.6%
1 650
44.5%
3 33
 
2.3%
0 9
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 768
52.6%
1 650
44.5%
3 33
 
2.3%
0 9
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 768
52.6%
1 650
44.5%
3 33
 
2.3%
0 9
 
0.6%

HalfBath
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
0
913 
1
535 
2
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 913
62.5%
1 535
36.6%
2 12
 
0.8%

Length

2025-01-27T18:27:41.905713image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T18:27:42.256355image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 913
62.5%
1 535
36.6%
2 12
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 913
62.5%
1 535
36.6%
2 12
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 913
62.5%
1 535
36.6%
2 12
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 913
62.5%
1 535
36.6%
2 12
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 913
62.5%
1 535
36.6%
2 12
 
0.8%

BedroomAbvGr
Real number (ℝ)

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8664384
Minimum0
Maximum8
Zeros6
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2025-01-27T18:27:42.598078image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median3
Q33
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.81577804
Coefficient of variation (CV)0.2845964
Kurtosis2.2308746
Mean2.8664384
Median Absolute Deviation (MAD)0
Skewness0.2117901
Sum4185
Variance0.66549382
MonotonicityNot monotonic
2025-01-27T18:27:43.124035image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 804
55.1%
2 358
24.5%
4 213
 
14.6%
1 50
 
3.4%
5 21
 
1.4%
6 7
 
0.5%
0 6
 
0.4%
8 1
 
0.1%
ValueCountFrequency (%)
0 6
 
0.4%
1 50
 
3.4%
2 358
24.5%
3 804
55.1%
4 213
 
14.6%
5 21
 
1.4%
6 7
 
0.5%
8 1
 
0.1%
ValueCountFrequency (%)
8 1
 
0.1%
6 7
 
0.5%
5 21
 
1.4%
4 213
 
14.6%
3 804
55.1%
2 358
24.5%
1 50
 
3.4%
0 6
 
0.4%

KitchenAbvGr
Categorical

Imbalance 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
1
1392 
2
 
65
3
 
2
0
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1392
95.3%
2 65
 
4.5%
3 2
 
0.1%
0 1
 
0.1%

Length

2025-01-27T18:27:43.645909image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T18:27:44.165567image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1 1392
95.3%
2 65
 
4.5%
3 2
 
0.1%
0 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 1392
95.3%
2 65
 
4.5%
3 2
 
0.1%
0 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1392
95.3%
2 65
 
4.5%
3 2
 
0.1%
0 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1392
95.3%
2 65
 
4.5%
3 2
 
0.1%
0 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1392
95.3%
2 65
 
4.5%
3 2
 
0.1%
0 1
 
0.1%

KitchenQual
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
Typical/Average
735 
Good
586 
Excellent
100 
Fair
 
39

Length

Max length15
Median length15
Mean length9.880137
Min length4

Characters and Unicode

Total characters14425
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGood
2nd rowTypical/Average
3rd rowGood
4th rowGood
5th rowGood

Common Values

ValueCountFrequency (%)
Typical/Average 735
50.3%
Good 586
40.1%
Excellent 100
 
6.8%
Fair 39
 
2.7%

Length

2025-01-27T18:27:44.614058image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T18:27:44.966323image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
typical/average 735
50.3%
good 586
40.1%
excellent 100
 
6.8%
fair 39
 
2.7%

Most occurring characters

ValueCountFrequency (%)
e 1670
 
11.6%
a 1509
 
10.5%
o 1172
 
8.1%
l 935
 
6.5%
c 835
 
5.8%
i 774
 
5.4%
r 774
 
5.4%
y 735
 
5.1%
g 735
 
5.1%
T 735
 
5.1%
Other values (11) 4551
31.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14425
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1670
 
11.6%
a 1509
 
10.5%
o 1172
 
8.1%
l 935
 
6.5%
c 835
 
5.8%
i 774
 
5.4%
r 774
 
5.4%
y 735
 
5.1%
g 735
 
5.1%
T 735
 
5.1%
Other values (11) 4551
31.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14425
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1670
 
11.6%
a 1509
 
10.5%
o 1172
 
8.1%
l 935
 
6.5%
c 835
 
5.8%
i 774
 
5.4%
r 774
 
5.4%
y 735
 
5.1%
g 735
 
5.1%
T 735
 
5.1%
Other values (11) 4551
31.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14425
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1670
 
11.6%
a 1509
 
10.5%
o 1172
 
8.1%
l 935
 
6.5%
c 835
 
5.8%
i 774
 
5.4%
r 774
 
5.4%
y 735
 
5.1%
g 735
 
5.1%
T 735
 
5.1%
Other values (11) 4551
31.5%

TotRmsAbvGrd
Real number (ℝ)

Distinct12
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5178082
Minimum2
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2025-01-27T18:27:45.401619image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q15
median6
Q37
95-th percentile10
Maximum14
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6253933
Coefficient of variation (CV)0.24937728
Kurtosis0.88076157
Mean6.5178082
Median Absolute Deviation (MAD)1
Skewness0.67634084
Sum9516
Variance2.6419033
MonotonicityNot monotonic
2025-01-27T18:27:45.821153image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 402
27.5%
7 329
22.5%
5 275
18.8%
8 187
12.8%
4 97
 
6.6%
9 75
 
5.1%
10 47
 
3.2%
11 18
 
1.2%
3 17
 
1.2%
12 11
 
0.8%
Other values (2) 2
 
0.1%
ValueCountFrequency (%)
2 1
 
0.1%
3 17
 
1.2%
4 97
 
6.6%
5 275
18.8%
6 402
27.5%
7 329
22.5%
8 187
12.8%
9 75
 
5.1%
10 47
 
3.2%
11 18
 
1.2%
ValueCountFrequency (%)
14 1
 
0.1%
12 11
 
0.8%
11 18
 
1.2%
10 47
 
3.2%
9 75
 
5.1%
8 187
12.8%
7 329
22.5%
6 402
27.5%
5 275
18.8%
4 97
 
6.6%

Functional
Categorical

Imbalance 

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
Typical Functionality
1360 
Minor Deductions 2
 
34
Minor Deductions 1
 
31
Moderate Deductions
 
15
Major Deductions 1
 
14
Other values (2)
 
6

Length

Max length21
Median length21
Mean length20.803425
Min length16

Characters and Unicode

Total characters30373
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowTypical Functionality
2nd rowTypical Functionality
3rd rowTypical Functionality
4th rowTypical Functionality
5th rowTypical Functionality

Common Values

ValueCountFrequency (%)
Typical Functionality 1360
93.2%
Minor Deductions 2 34
 
2.3%
Minor Deductions 1 31
 
2.1%
Moderate Deductions 15
 
1.0%
Major Deductions 1 14
 
1.0%
Major Deductions 2 5
 
0.3%
Severely Damaged 1
 
0.1%

Length

2025-01-27T18:27:46.453474image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T18:27:47.013841image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
typical 1360
45.3%
functionality 1360
45.3%
deductions 99
 
3.3%
minor 65
 
2.2%
1 45
 
1.5%
2 39
 
1.3%
major 19
 
0.6%
moderate 15
 
0.5%
severely 1
 
< 0.1%
damaged 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
i 4244
14.0%
n 2884
9.5%
t 2834
9.3%
c 2819
9.3%
a 2756
9.1%
y 2721
9.0%
l 2721
9.0%
o 1558
 
5.1%
1544
 
5.1%
u 1459
 
4.8%
Other values (16) 4833
15.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30373
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 4244
14.0%
n 2884
9.5%
t 2834
9.3%
c 2819
9.3%
a 2756
9.1%
y 2721
9.0%
l 2721
9.0%
o 1558
 
5.1%
1544
 
5.1%
u 1459
 
4.8%
Other values (16) 4833
15.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30373
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 4244
14.0%
n 2884
9.5%
t 2834
9.3%
c 2819
9.3%
a 2756
9.1%
y 2721
9.0%
l 2721
9.0%
o 1558
 
5.1%
1544
 
5.1%
u 1459
 
4.8%
Other values (16) 4833
15.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30373
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 4244
14.0%
n 2884
9.5%
t 2834
9.3%
c 2819
9.3%
a 2756
9.1%
y 2721
9.0%
l 2721
9.0%
o 1558
 
5.1%
1544
 
5.1%
u 1459
 
4.8%
Other values (16) 4833
15.9%

Fireplaces
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
0
690 
1
650 
2
115 
3
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 690
47.3%
1 650
44.5%
2 115
 
7.9%
3 5
 
0.3%

Length

2025-01-27T18:27:47.739020image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T18:27:48.315917image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 690
47.3%
1 650
44.5%
2 115
 
7.9%
3 5
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 690
47.3%
1 650
44.5%
2 115
 
7.9%
3 5
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 690
47.3%
1 650
44.5%
2 115
 
7.9%
3 5
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 690
47.3%
1 650
44.5%
2 115
 
7.9%
3 5
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 690
47.3%
1 650
44.5%
2 115
 
7.9%
3 5
 
0.3%

FireplaceQu
Categorical

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
nan
690 
Good - Masonry Fireplace in main level
380 
Average - Prefabricated Fireplace in main living area or Masonry Fireplace in basement
313 
Fair - Prefabricated Fireplace in basement
 
33
Excellent - Exceptional Masonry Fireplace
 
24

Length

Max length86
Median length42
Mean length31.710959
Min length3

Characters and Unicode

Total characters46298
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownan
2nd rowAverage - Prefabricated Fireplace in main living area or Masonry Fireplace in basement
3rd rowAverage - Prefabricated Fireplace in main living area or Masonry Fireplace in basement
4th rowGood - Masonry Fireplace in main level
5th rowAverage - Prefabricated Fireplace in main living area or Masonry Fireplace in basement

Common Values

ValueCountFrequency (%)
nan 690
47.3%
Good - Masonry Fireplace in main level 380
26.0%
Average - Prefabricated Fireplace in main living area or Masonry Fireplace in basement 313
21.4%
Fair - Prefabricated Fireplace in basement 33
 
2.3%
Excellent - Exceptional Masonry Fireplace 24
 
1.6%
Poor - Ben Franklin Stove 20
 
1.4%

Length

2025-01-27T18:27:48.727393image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T18:27:49.189941image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
fireplace 1063
13.6%
in 1039
13.3%
770
9.8%
masonry 717
9.1%
main 693
8.8%
nan 690
8.8%
level 380
 
4.8%
good 380
 
4.8%
basement 346
 
4.4%
prefabricated 346
 
4.4%
Other values (11) 1413
18.0%

Most occurring characters

ValueCountFrequency (%)
6377
13.8%
e 5321
11.5%
a 5217
11.3%
n 4596
9.9%
i 3844
 
8.3%
r 3484
 
7.5%
l 2228
 
4.8%
o 1874
 
4.0%
c 1457
 
3.1%
F 1116
 
2.4%
Other values (20) 10784
23.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 46298
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6377
13.8%
e 5321
11.5%
a 5217
11.3%
n 4596
9.9%
i 3844
 
8.3%
r 3484
 
7.5%
l 2228
 
4.8%
o 1874
 
4.0%
c 1457
 
3.1%
F 1116
 
2.4%
Other values (20) 10784
23.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 46298
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6377
13.8%
e 5321
11.5%
a 5217
11.3%
n 4596
9.9%
i 3844
 
8.3%
r 3484
 
7.5%
l 2228
 
4.8%
o 1874
 
4.0%
c 1457
 
3.1%
F 1116
 
2.4%
Other values (20) 10784
23.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 46298
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6377
13.8%
e 5321
11.5%
a 5217
11.3%
n 4596
9.9%
i 3844
 
8.3%
r 3484
 
7.5%
l 2228
 
4.8%
o 1874
 
4.0%
c 1457
 
3.1%
F 1116
 
2.4%
Other values (20) 10784
23.3%

GarageType
Categorical

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
Attached to home
870 
Detached from home
387 
Built-In (Garage part of house - typically has room above garage)
88 
nan
 
81
Basement Garage
 
19
Other values (2)
 
15

Length

Max length65
Median length16
Mean length18.749315
Min length3

Characters and Unicode

Total characters27374
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAttached to home
2nd rowAttached to home
3rd rowAttached to home
4th rowDetached from home
5th rowAttached to home

Common Values

ValueCountFrequency (%)
Attached to home 870
59.6%
Detached from home 387
26.5%
Built-In (Garage part of house - typically has room above garage) 88
 
6.0%
nan 81
 
5.5%
Basement Garage 19
 
1.3%
Car Port 9
 
0.6%
More than one type of garage 6
 
0.4%

Length

2025-01-27T18:27:50.081115image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T18:27:51.109894image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
home 1257
25.6%
attached 870
17.7%
to 870
17.7%
detached 387
 
7.9%
from 387
 
7.9%
garage 201
 
4.1%
of 94
 
1.9%
above 88
 
1.8%
room 88
 
1.8%
has 88
 
1.8%
Other values (13) 582
11.8%

Most occurring characters

ValueCountFrequency (%)
3452
12.6%
e 3334
12.2%
t 3301
12.1%
o 2981
10.9%
h 2696
9.8%
a 2126
7.8%
m 1751
6.4%
c 1345
 
4.9%
d 1257
 
4.6%
A 870
 
3.2%
Other values (22) 4261
15.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 27374
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3452
12.6%
e 3334
12.2%
t 3301
12.1%
o 2981
10.9%
h 2696
9.8%
a 2126
7.8%
m 1751
6.4%
c 1345
 
4.9%
d 1257
 
4.6%
A 870
 
3.2%
Other values (22) 4261
15.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 27374
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3452
12.6%
e 3334
12.2%
t 3301
12.1%
o 2981
10.9%
h 2696
9.8%
a 2126
7.8%
m 1751
6.4%
c 1345
 
4.9%
d 1257
 
4.6%
A 870
 
3.2%
Other values (22) 4261
15.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 27374
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3452
12.6%
e 3334
12.2%
t 3301
12.1%
o 2981
10.9%
h 2696
9.8%
a 2126
7.8%
m 1751
6.4%
c 1345
 
4.9%
d 1257
 
4.6%
A 870
 
3.2%
Other values (22) 4261
15.6%

GarageYrBlt
Real number (ℝ)

Missing 

Distinct97
Distinct (%)7.0%
Missing81
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean1978.5062
Minimum1900
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2025-01-27T18:27:52.069980image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1930
Q11961
median1980
Q32002
95-th percentile2007
Maximum2010
Range110
Interquartile range (IQR)41

Descriptive statistics

Standard deviation24.689725
Coefficient of variation (CV)0.012478973
Kurtosis-0.418341
Mean1978.5062
Median Absolute Deviation (MAD)21
Skewness-0.64941462
Sum2728360
Variance609.58251
MonotonicityNot monotonic
2025-01-27T18:27:52.927856image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2005 65
 
4.5%
2006 59
 
4.0%
2004 53
 
3.6%
2003 50
 
3.4%
2007 49
 
3.4%
1977 35
 
2.4%
1998 31
 
2.1%
1999 30
 
2.1%
1976 29
 
2.0%
2008 29
 
2.0%
Other values (87) 949
65.0%
(Missing) 81
 
5.5%
ValueCountFrequency (%)
1900 1
 
0.1%
1906 1
 
0.1%
1908 1
 
0.1%
1910 3
 
0.2%
1914 2
 
0.1%
1915 2
 
0.1%
1916 5
 
0.3%
1918 2
 
0.1%
1920 14
1.0%
1921 3
 
0.2%
ValueCountFrequency (%)
2010 3
 
0.2%
2009 21
 
1.4%
2008 29
2.0%
2007 49
3.4%
2006 59
4.0%
2005 65
4.5%
2004 53
3.6%
2003 50
3.4%
2002 26
 
1.8%
2001 20
 
1.4%

GarageFinish
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
Unfinished
605 
Rough Finished
422 
Finished
352 
nan
81 

Length

Max length14
Median length10
Mean length10.285616
Min length3

Characters and Unicode

Total characters15017
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRough Finished
2nd rowRough Finished
3rd rowRough Finished
4th rowUnfinished
5th rowRough Finished

Common Values

ValueCountFrequency (%)
Unfinished 605
41.4%
Rough Finished 422
28.9%
Finished 352
24.1%
nan 81
 
5.5%

Length

2025-01-27T18:27:53.893083image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T18:27:54.559720image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
finished 774
41.1%
unfinished 605
32.1%
rough 422
22.4%
nan 81
 
4.3%

Most occurring characters

ValueCountFrequency (%)
i 2758
18.4%
n 2146
14.3%
h 1801
12.0%
s 1379
9.2%
e 1379
9.2%
d 1379
9.2%
F 774
 
5.2%
U 605
 
4.0%
f 605
 
4.0%
R 422
 
2.8%
Other values (5) 1769
11.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15017
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 2758
18.4%
n 2146
14.3%
h 1801
12.0%
s 1379
9.2%
e 1379
9.2%
d 1379
9.2%
F 774
 
5.2%
U 605
 
4.0%
f 605
 
4.0%
R 422
 
2.8%
Other values (5) 1769
11.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15017
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 2758
18.4%
n 2146
14.3%
h 1801
12.0%
s 1379
9.2%
e 1379
9.2%
d 1379
9.2%
F 774
 
5.2%
U 605
 
4.0%
f 605
 
4.0%
R 422
 
2.8%
Other values (5) 1769
11.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15017
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 2758
18.4%
n 2146
14.3%
h 1801
12.0%
s 1379
9.2%
e 1379
9.2%
d 1379
9.2%
F 774
 
5.2%
U 605
 
4.0%
f 605
 
4.0%
R 422
 
2.8%
Other values (5) 1769
11.8%

GarageCars
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
2
824 
1
369 
3
181 
0
 
81
4
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
2 824
56.4%
1 369
25.3%
3 181
 
12.4%
0 81
 
5.5%
4 5
 
0.3%

Length

2025-01-27T18:27:55.341588image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T18:27:56.017904image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
2 824
56.4%
1 369
25.3%
3 181
 
12.4%
0 81
 
5.5%
4 5
 
0.3%

Most occurring characters

ValueCountFrequency (%)
2 824
56.4%
1 369
25.3%
3 181
 
12.4%
0 81
 
5.5%
4 5
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 824
56.4%
1 369
25.3%
3 181
 
12.4%
0 81
 
5.5%
4 5
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 824
56.4%
1 369
25.3%
3 181
 
12.4%
0 81
 
5.5%
4 5
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1460
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 824
56.4%
1 369
25.3%
3 181
 
12.4%
0 81
 
5.5%
4 5
 
0.3%

GarageArea
Real number (ℝ)

Zeros 

Distinct441
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean472.98014
Minimum0
Maximum1418
Zeros81
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2025-01-27T18:27:56.768013image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1334.5
median480
Q3576
95-th percentile850.1
Maximum1418
Range1418
Interquartile range (IQR)241.5

Descriptive statistics

Standard deviation213.80484
Coefficient of variation (CV)0.45203768
Kurtosis0.9170672
Mean472.98014
Median Absolute Deviation (MAD)120
Skewness0.17998091
Sum690551
Variance45712.51
MonotonicityNot monotonic
2025-01-27T18:27:57.687660image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 81
 
5.5%
440 49
 
3.4%
576 47
 
3.2%
240 38
 
2.6%
484 34
 
2.3%
528 33
 
2.3%
288 27
 
1.8%
400 25
 
1.7%
264 24
 
1.6%
480 24
 
1.6%
Other values (431) 1078
73.8%
ValueCountFrequency (%)
0 81
5.5%
160 2
 
0.1%
164 1
 
0.1%
180 9
 
0.6%
186 1
 
0.1%
189 1
 
0.1%
192 1
 
0.1%
198 1
 
0.1%
200 4
 
0.3%
205 3
 
0.2%
ValueCountFrequency (%)
1418 1
0.1%
1390 1
0.1%
1356 1
0.1%
1248 1
0.1%
1220 1
0.1%
1166 1
0.1%
1134 1
0.1%
1069 1
0.1%
1053 1
0.1%
1052 2
0.1%

GarageQual
Categorical

Imbalance 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
Typical/Average
1311 
nan
 
81
Fair
 
48
Good
 
14
Excellent
 
3

Length

Max length15
Median length15
Mean length13.832192
Min length3

Characters and Unicode

Total characters20195
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTypical/Average
2nd rowTypical/Average
3rd rowTypical/Average
4th rowTypical/Average
5th rowTypical/Average

Common Values

ValueCountFrequency (%)
Typical/Average 1311
89.8%
nan 81
 
5.5%
Fair 48
 
3.3%
Good 14
 
1.0%
Excellent 3
 
0.2%
Poor 3
 
0.2%

Length

2025-01-27T18:27:58.593291image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T18:27:59.361849image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
typical/average 1311
89.8%
nan 81
 
5.5%
fair 48
 
3.3%
good 14
 
1.0%
excellent 3
 
0.2%
poor 3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
a 2751
13.6%
e 2628
13.0%
r 1362
 
6.7%
i 1359
 
6.7%
l 1317
 
6.5%
c 1314
 
6.5%
v 1311
 
6.5%
g 1311
 
6.5%
y 1311
 
6.5%
T 1311
 
6.5%
Other values (12) 4220
20.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20195
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2751
13.6%
e 2628
13.0%
r 1362
 
6.7%
i 1359
 
6.7%
l 1317
 
6.5%
c 1314
 
6.5%
v 1311
 
6.5%
g 1311
 
6.5%
y 1311
 
6.5%
T 1311
 
6.5%
Other values (12) 4220
20.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20195
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2751
13.6%
e 2628
13.0%
r 1362
 
6.7%
i 1359
 
6.7%
l 1317
 
6.5%
c 1314
 
6.5%
v 1311
 
6.5%
g 1311
 
6.5%
y 1311
 
6.5%
T 1311
 
6.5%
Other values (12) 4220
20.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20195
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2751
13.6%
e 2628
13.0%
r 1362
 
6.7%
i 1359
 
6.7%
l 1317
 
6.5%
c 1314
 
6.5%
v 1311
 
6.5%
g 1311
 
6.5%
y 1311
 
6.5%
T 1311
 
6.5%
Other values (12) 4220
20.9%

GarageCond
Categorical

Imbalance 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
Typical/Average
1326 
nan
 
81
Fair
 
35
Good
 
9
Poor
 
7

Length

Max length15
Median length15
Mean length13.941781
Min length3

Characters and Unicode

Total characters20355
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTypical/Average
2nd rowTypical/Average
3rd rowTypical/Average
4th rowTypical/Average
5th rowTypical/Average

Common Values

ValueCountFrequency (%)
Typical/Average 1326
90.8%
nan 81
 
5.5%
Fair 35
 
2.4%
Good 9
 
0.6%
Poor 7
 
0.5%
Excellent 2
 
0.1%

Length

2025-01-27T18:28:00.166208image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T18:28:00.786421image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
typical/average 1326
90.8%
nan 81
 
5.5%
fair 35
 
2.4%
good 9
 
0.6%
poor 7
 
0.5%
excellent 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
a 2768
13.6%
e 2656
13.0%
r 1368
 
6.7%
i 1361
 
6.7%
l 1330
 
6.5%
c 1328
 
6.5%
v 1326
 
6.5%
g 1326
 
6.5%
y 1326
 
6.5%
T 1326
 
6.5%
Other values (12) 4240
20.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20355
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2768
13.6%
e 2656
13.0%
r 1368
 
6.7%
i 1361
 
6.7%
l 1330
 
6.5%
c 1328
 
6.5%
v 1326
 
6.5%
g 1326
 
6.5%
y 1326
 
6.5%
T 1326
 
6.5%
Other values (12) 4240
20.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20355
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2768
13.6%
e 2656
13.0%
r 1368
 
6.7%
i 1361
 
6.7%
l 1330
 
6.5%
c 1328
 
6.5%
v 1326
 
6.5%
g 1326
 
6.5%
y 1326
 
6.5%
T 1326
 
6.5%
Other values (12) 4240
20.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20355
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2768
13.6%
e 2656
13.0%
r 1368
 
6.7%
i 1361
 
6.7%
l 1330
 
6.5%
c 1328
 
6.5%
v 1326
 
6.5%
g 1326
 
6.5%
y 1326
 
6.5%
T 1326
 
6.5%
Other values (12) 4240
20.8%

PavedDrive
Categorical

Imbalance 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
Paved
1340 
Dirt/Gravel
 
90
Partial Pavement
 
30

Length

Max length16
Median length5
Mean length5.5958904
Min length5

Characters and Unicode

Total characters8170
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPaved
2nd rowPaved
3rd rowPaved
4th rowPaved
5th rowPaved

Common Values

ValueCountFrequency (%)
Paved 1340
91.8%
Dirt/Gravel 90
 
6.2%
Partial Pavement 30
 
2.1%

Length

2025-01-27T18:28:01.588061image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T18:28:02.176054image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
paved 1340
89.9%
dirt/gravel 90
 
6.0%
partial 30
 
2.0%
pavement 30
 
2.0%

Most occurring characters

ValueCountFrequency (%)
a 1520
18.6%
e 1490
18.2%
v 1460
17.9%
P 1400
17.1%
d 1340
16.4%
r 210
 
2.6%
t 150
 
1.8%
i 120
 
1.5%
l 120
 
1.5%
D 90
 
1.1%
Other values (5) 270
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8170
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1520
18.6%
e 1490
18.2%
v 1460
17.9%
P 1400
17.1%
d 1340
16.4%
r 210
 
2.6%
t 150
 
1.8%
i 120
 
1.5%
l 120
 
1.5%
D 90
 
1.1%
Other values (5) 270
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8170
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1520
18.6%
e 1490
18.2%
v 1460
17.9%
P 1400
17.1%
d 1340
16.4%
r 210
 
2.6%
t 150
 
1.8%
i 120
 
1.5%
l 120
 
1.5%
D 90
 
1.1%
Other values (5) 270
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8170
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1520
18.6%
e 1490
18.2%
v 1460
17.9%
P 1400
17.1%
d 1340
16.4%
r 210
 
2.6%
t 150
 
1.8%
i 120
 
1.5%
l 120
 
1.5%
D 90
 
1.1%
Other values (5) 270
 
3.3%

WoodDeckSF
Real number (ℝ)

Zeros 

Distinct274
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.244521
Minimum0
Maximum857
Zeros761
Zeros (%)52.1%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2025-01-27T18:28:02.807981image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3168
95-th percentile335
Maximum857
Range857
Interquartile range (IQR)168

Descriptive statistics

Standard deviation125.33879
Coefficient of variation (CV)1.3299319
Kurtosis2.9929509
Mean94.244521
Median Absolute Deviation (MAD)0
Skewness1.5413758
Sum137597
Variance15709.813
MonotonicityNot monotonic
2025-01-27T18:28:03.648744image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 761
52.1%
192 38
 
2.6%
100 36
 
2.5%
144 33
 
2.3%
120 31
 
2.1%
168 28
 
1.9%
140 15
 
1.0%
224 14
 
1.0%
208 10
 
0.7%
240 10
 
0.7%
Other values (264) 484
33.2%
ValueCountFrequency (%)
0 761
52.1%
12 2
 
0.1%
24 2
 
0.1%
26 2
 
0.1%
28 2
 
0.1%
30 1
 
0.1%
32 1
 
0.1%
33 1
 
0.1%
35 1
 
0.1%
36 4
 
0.3%
ValueCountFrequency (%)
857 1
0.1%
736 1
0.1%
728 1
0.1%
670 1
0.1%
668 1
0.1%
635 1
0.1%
586 1
0.1%
576 1
0.1%
574 1
0.1%
550 1
0.1%

OpenPorchSF
Real number (ℝ)

Zeros 

Distinct202
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.660274
Minimum0
Maximum547
Zeros656
Zeros (%)44.9%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2025-01-27T18:28:04.343008image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median25
Q368
95-th percentile175.05
Maximum547
Range547
Interquartile range (IQR)68

Descriptive statistics

Standard deviation66.256028
Coefficient of variation (CV)1.4199665
Kurtosis8.4903358
Mean46.660274
Median Absolute Deviation (MAD)25
Skewness2.3643417
Sum68124
Variance4389.8612
MonotonicityNot monotonic
2025-01-27T18:28:05.365332image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 656
44.9%
36 29
 
2.0%
48 22
 
1.5%
20 21
 
1.4%
40 19
 
1.3%
45 19
 
1.3%
24 16
 
1.1%
30 16
 
1.1%
60 15
 
1.0%
39 14
 
1.0%
Other values (192) 633
43.4%
ValueCountFrequency (%)
0 656
44.9%
4 1
 
0.1%
8 1
 
0.1%
10 1
 
0.1%
11 1
 
0.1%
12 3
 
0.2%
15 1
 
0.1%
16 8
 
0.5%
17 2
 
0.1%
18 5
 
0.3%
ValueCountFrequency (%)
547 1
0.1%
523 1
0.1%
502 1
0.1%
418 1
0.1%
406 1
0.1%
364 1
0.1%
341 1
0.1%
319 1
0.1%
312 2
0.1%
304 1
0.1%

EnclosedPorch
Real number (ℝ)

Zeros 

Distinct120
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.95411
Minimum0
Maximum552
Zeros1252
Zeros (%)85.8%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2025-01-27T18:28:06.278032image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile180.15
Maximum552
Range552
Interquartile range (IQR)0

Descriptive statistics

Standard deviation61.119149
Coefficient of variation (CV)2.7839502
Kurtosis10.430766
Mean21.95411
Median Absolute Deviation (MAD)0
Skewness3.0898719
Sum32053
Variance3735.5503
MonotonicityNot monotonic
2025-01-27T18:28:07.398725image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1252
85.8%
112 15
 
1.0%
96 6
 
0.4%
192 5
 
0.3%
144 5
 
0.3%
120 5
 
0.3%
216 5
 
0.3%
156 4
 
0.3%
116 4
 
0.3%
252 4
 
0.3%
Other values (110) 155
 
10.6%
ValueCountFrequency (%)
0 1252
85.8%
19 1
 
0.1%
20 1
 
0.1%
24 1
 
0.1%
30 1
 
0.1%
32 2
 
0.1%
34 2
 
0.1%
36 2
 
0.1%
37 1
 
0.1%
39 2
 
0.1%
ValueCountFrequency (%)
552 1
0.1%
386 1
0.1%
330 1
0.1%
318 1
0.1%
301 1
0.1%
294 1
0.1%
293 1
0.1%
291 1
0.1%
286 1
0.1%
280 1
0.1%

3SsnPorch
Real number (ℝ)

Zeros 

Distinct20
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.409589
Minimum0
Maximum508
Zeros1436
Zeros (%)98.4%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2025-01-27T18:28:08.150615image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum508
Range508
Interquartile range (IQR)0

Descriptive statistics

Standard deviation29.317331
Coefficient of variation (CV)8.5984939
Kurtosis123.66238
Mean3.409589
Median Absolute Deviation (MAD)0
Skewness10.304342
Sum4978
Variance859.50587
MonotonicityNot monotonic
2025-01-27T18:28:09.357326image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 1436
98.4%
168 3
 
0.2%
144 2
 
0.1%
180 2
 
0.1%
216 2
 
0.1%
290 1
 
0.1%
153 1
 
0.1%
96 1
 
0.1%
23 1
 
0.1%
162 1
 
0.1%
Other values (10) 10
 
0.7%
ValueCountFrequency (%)
0 1436
98.4%
23 1
 
0.1%
96 1
 
0.1%
130 1
 
0.1%
140 1
 
0.1%
144 2
 
0.1%
153 1
 
0.1%
162 1
 
0.1%
168 3
 
0.2%
180 2
 
0.1%
ValueCountFrequency (%)
508 1
0.1%
407 1
0.1%
320 1
0.1%
304 1
0.1%
290 1
0.1%
245 1
0.1%
238 1
0.1%
216 2
0.1%
196 1
0.1%
182 1
0.1%

ScreenPorch
Real number (ℝ)

Zeros 

Distinct76
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.060959
Minimum0
Maximum480
Zeros1344
Zeros (%)92.1%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2025-01-27T18:28:10.051199image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile160
Maximum480
Range480
Interquartile range (IQR)0

Descriptive statistics

Standard deviation55.757415
Coefficient of variation (CV)3.7021159
Kurtosis18.439068
Mean15.060959
Median Absolute Deviation (MAD)0
Skewness4.1222137
Sum21989
Variance3108.8894
MonotonicityNot monotonic
2025-01-27T18:28:11.167966image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1344
92.1%
192 6
 
0.4%
120 5
 
0.3%
224 5
 
0.3%
189 4
 
0.3%
180 4
 
0.3%
147 3
 
0.2%
90 3
 
0.2%
160 3
 
0.2%
144 3
 
0.2%
Other values (66) 80
 
5.5%
ValueCountFrequency (%)
0 1344
92.1%
40 1
 
0.1%
53 1
 
0.1%
60 1
 
0.1%
63 1
 
0.1%
80 1
 
0.1%
90 3
 
0.2%
95 1
 
0.1%
99 1
 
0.1%
100 2
 
0.1%
ValueCountFrequency (%)
480 1
0.1%
440 1
0.1%
410 1
0.1%
396 1
0.1%
385 1
0.1%
374 1
0.1%
322 1
0.1%
312 1
0.1%
291 1
0.1%
288 2
0.1%

PoolArea
Real number (ℝ)

Zeros 

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7589041
Minimum0
Maximum738
Zeros1453
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2025-01-27T18:28:11.828140image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum738
Range738
Interquartile range (IQR)0

Descriptive statistics

Standard deviation40.177307
Coefficient of variation (CV)14.562778
Kurtosis223.2685
Mean2.7589041
Median Absolute Deviation (MAD)0
Skewness14.828374
Sum4028
Variance1614.216
MonotonicityNot monotonic
2025-01-27T18:28:12.508752image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 1453
99.5%
512 1
 
0.1%
648 1
 
0.1%
576 1
 
0.1%
555 1
 
0.1%
480 1
 
0.1%
519 1
 
0.1%
738 1
 
0.1%
ValueCountFrequency (%)
0 1453
99.5%
480 1
 
0.1%
512 1
 
0.1%
519 1
 
0.1%
555 1
 
0.1%
576 1
 
0.1%
648 1
 
0.1%
738 1
 
0.1%
ValueCountFrequency (%)
738 1
 
0.1%
648 1
 
0.1%
576 1
 
0.1%
555 1
 
0.1%
519 1
 
0.1%
512 1
 
0.1%
480 1
 
0.1%
0 1453
99.5%

PoolQC
Categorical

Imbalance 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
nan
1453 
Good
 
3
Excellent
 
2
Fair
 
2

Length

Max length9
Median length3
Mean length3.0116438
Min length3

Characters and Unicode

Total characters4397
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan

Common Values

ValueCountFrequency (%)
nan 1453
99.5%
Good 3
 
0.2%
Excellent 2
 
0.1%
Fair 2
 
0.1%

Length

2025-01-27T18:28:13.399576image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T18:28:14.036230image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
nan 1453
99.5%
good 3
 
0.2%
excellent 2
 
0.1%
fair 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
n 2908
66.1%
a 1455
33.1%
o 6
 
0.1%
e 4
 
0.1%
l 4
 
0.1%
G 3
 
0.1%
d 3
 
0.1%
E 2
 
< 0.1%
x 2
 
< 0.1%
c 2
 
< 0.1%
Other values (4) 8
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4397
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 2908
66.1%
a 1455
33.1%
o 6
 
0.1%
e 4
 
0.1%
l 4
 
0.1%
G 3
 
0.1%
d 3
 
0.1%
E 2
 
< 0.1%
x 2
 
< 0.1%
c 2
 
< 0.1%
Other values (4) 8
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4397
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 2908
66.1%
a 1455
33.1%
o 6
 
0.1%
e 4
 
0.1%
l 4
 
0.1%
G 3
 
0.1%
d 3
 
0.1%
E 2
 
< 0.1%
x 2
 
< 0.1%
c 2
 
< 0.1%
Other values (4) 8
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4397
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 2908
66.1%
a 1455
33.1%
o 6
 
0.1%
e 4
 
0.1%
l 4
 
0.1%
G 3
 
0.1%
d 3
 
0.1%
E 2
 
< 0.1%
x 2
 
< 0.1%
c 2
 
< 0.1%
Other values (4) 8
 
0.2%

Fence
Categorical

Imbalance 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
nan
1179 
Minimum Privacy
157 
Good Privacy
 
59
Good Wood
 
54
Minimum Wood/Wire
 
11

Length

Max length17
Median length3
Mean length4.9815068
Min length3

Characters and Unicode

Total characters7273
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan

Common Values

ValueCountFrequency (%)
nan 1179
80.8%
Minimum Privacy 157
 
10.8%
Good Privacy 59
 
4.0%
Good Wood 54
 
3.7%
Minimum Wood/Wire 11
 
0.8%

Length

2025-01-27T18:28:14.855990image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T18:28:15.566128image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
nan 1179
67.7%
privacy 216
 
12.4%
minimum 168
 
9.6%
good 113
 
6.5%
wood 54
 
3.1%
wood/wire 11
 
0.6%

Most occurring characters

ValueCountFrequency (%)
n 2526
34.7%
a 1395
19.2%
i 563
 
7.7%
o 356
 
4.9%
m 336
 
4.6%
281
 
3.9%
r 227
 
3.1%
y 216
 
3.0%
c 216
 
3.0%
v 216
 
3.0%
Other values (8) 941
 
12.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7273
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 2526
34.7%
a 1395
19.2%
i 563
 
7.7%
o 356
 
4.9%
m 336
 
4.6%
281
 
3.9%
r 227
 
3.1%
y 216
 
3.0%
c 216
 
3.0%
v 216
 
3.0%
Other values (8) 941
 
12.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7273
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 2526
34.7%
a 1395
19.2%
i 563
 
7.7%
o 356
 
4.9%
m 336
 
4.6%
281
 
3.9%
r 227
 
3.1%
y 216
 
3.0%
c 216
 
3.0%
v 216
 
3.0%
Other values (8) 941
 
12.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7273
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 2526
34.7%
a 1395
19.2%
i 563
 
7.7%
o 356
 
4.9%
m 336
 
4.6%
281
 
3.9%
r 227
 
3.1%
y 216
 
3.0%
c 216
 
3.0%
v 216
 
3.0%
Other values (8) 941
 
12.9%

MiscFeature
Categorical

Imbalance 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
nan
1406 
Shed (over 100 SF)
 
49
2nd Garage (if not described in garage section)
 
2
Other
 
2
Tennis Court
 
1

Length

Max length47
Median length3
Mean length3.5726027
Min length3

Characters and Unicode

Total characters5216
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan

Common Values

ValueCountFrequency (%)
nan 1406
96.3%
Shed (over 100 SF) 49
 
3.4%
2nd Garage (if not described in garage section) 2
 
0.1%
Other 2
 
0.1%
Tennis Court 1
 
0.1%

Length

2025-01-27T18:28:16.466865image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T18:28:17.176041image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
nan 1406
86.7%
shed 49
 
3.0%
over 49
 
3.0%
100 49
 
3.0%
sf 49
 
3.0%
garage 4
 
0.2%
2nd 2
 
0.1%
if 2
 
0.1%
not 2
 
0.1%
described 2
 
0.1%
Other values (5) 8
 
0.5%

Most occurring characters

ValueCountFrequency (%)
n 2822
54.1%
a 1414
27.1%
162
 
3.1%
e 111
 
2.1%
S 98
 
1.9%
0 98
 
1.9%
r 58
 
1.1%
d 55
 
1.1%
o 54
 
1.0%
) 51
 
1.0%
Other values (18) 293
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5216
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 2822
54.1%
a 1414
27.1%
162
 
3.1%
e 111
 
2.1%
S 98
 
1.9%
0 98
 
1.9%
r 58
 
1.1%
d 55
 
1.1%
o 54
 
1.0%
) 51
 
1.0%
Other values (18) 293
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5216
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 2822
54.1%
a 1414
27.1%
162
 
3.1%
e 111
 
2.1%
S 98
 
1.9%
0 98
 
1.9%
r 58
 
1.1%
d 55
 
1.1%
o 54
 
1.0%
) 51
 
1.0%
Other values (18) 293
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5216
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 2822
54.1%
a 1414
27.1%
162
 
3.1%
e 111
 
2.1%
S 98
 
1.9%
0 98
 
1.9%
r 58
 
1.1%
d 55
 
1.1%
o 54
 
1.0%
) 51
 
1.0%
Other values (18) 293
 
5.6%

MiscVal
Real number (ℝ)

Skewed  Zeros 

Distinct21
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.489041
Minimum0
Maximum15500
Zeros1408
Zeros (%)96.4%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2025-01-27T18:28:17.844116image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum15500
Range15500
Interquartile range (IQR)0

Descriptive statistics

Standard deviation496.12302
Coefficient of variation (CV)11.408001
Kurtosis701.00334
Mean43.489041
Median Absolute Deviation (MAD)0
Skewness24.476794
Sum63494
Variance246138.06
MonotonicityNot monotonic
2025-01-27T18:28:18.631727image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 1408
96.4%
400 11
 
0.8%
500 8
 
0.5%
700 5
 
0.3%
450 4
 
0.3%
600 4
 
0.3%
2000 4
 
0.3%
1200 2
 
0.1%
480 2
 
0.1%
15500 1
 
0.1%
Other values (11) 11
 
0.8%
ValueCountFrequency (%)
0 1408
96.4%
54 1
 
0.1%
350 1
 
0.1%
400 11
 
0.8%
450 4
 
0.3%
480 2
 
0.1%
500 8
 
0.5%
560 1
 
0.1%
600 4
 
0.3%
620 1
 
0.1%
ValueCountFrequency (%)
15500 1
 
0.1%
8300 1
 
0.1%
3500 1
 
0.1%
2500 1
 
0.1%
2000 4
0.3%
1400 1
 
0.1%
1300 1
 
0.1%
1200 2
0.1%
1150 1
 
0.1%
800 1
 
0.1%

MoSold
Real number (ℝ)

Distinct12
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3219178
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2025-01-27T18:28:19.321494image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median6
Q38
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.7036262
Coefficient of variation (CV)0.42765918
Kurtosis-0.40410934
Mean6.3219178
Median Absolute Deviation (MAD)2
Skewness0.21205299
Sum9230
Variance7.3095947
MonotonicityNot monotonic
2025-01-27T18:28:19.891344image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 253
17.3%
7 234
16.0%
5 204
14.0%
4 141
9.7%
8 122
8.4%
3 106
7.3%
10 89
 
6.1%
11 79
 
5.4%
9 63
 
4.3%
12 59
 
4.0%
Other values (2) 110
7.5%
ValueCountFrequency (%)
1 58
 
4.0%
2 52
 
3.6%
3 106
7.3%
4 141
9.7%
5 204
14.0%
6 253
17.3%
7 234
16.0%
8 122
8.4%
9 63
 
4.3%
10 89
 
6.1%
ValueCountFrequency (%)
12 59
 
4.0%
11 79
 
5.4%
10 89
 
6.1%
9 63
 
4.3%
8 122
8.4%
7 234
16.0%
6 253
17.3%
5 204
14.0%
4 141
9.7%
3 106
7.3%

YrSold
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
2009
338 
2007
329 
2006
314 
2008
304 
2010
175 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters5840
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2008
2nd row2007
3rd row2008
4th row2006
5th row2008

Common Values

ValueCountFrequency (%)
2009 338
23.2%
2007 329
22.5%
2006 314
21.5%
2008 304
20.8%
2010 175
12.0%

Length

2025-01-27T18:28:20.864922image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T18:28:21.507509image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
2009 338
23.2%
2007 329
22.5%
2006 314
21.5%
2008 304
20.8%
2010 175
12.0%

Most occurring characters

ValueCountFrequency (%)
0 2920
50.0%
2 1460
25.0%
9 338
 
5.8%
7 329
 
5.6%
6 314
 
5.4%
8 304
 
5.2%
1 175
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5840
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2920
50.0%
2 1460
25.0%
9 338
 
5.8%
7 329
 
5.6%
6 314
 
5.4%
8 304
 
5.2%
1 175
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5840
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2920
50.0%
2 1460
25.0%
9 338
 
5.8%
7 329
 
5.6%
6 314
 
5.4%
8 304
 
5.2%
1 175
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5840
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2920
50.0%
2 1460
25.0%
9 338
 
5.8%
7 329
 
5.6%
6 314
 
5.4%
8 304
 
5.2%
1 175
 
3.0%

SaleType
Categorical

Imbalance 

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
Warranty Deed - Conventional
1267 
Home just constructed and sold
 
122
Court Officer Deed/Estate
 
43
Contract Low Down
 
9
Contract Low Interest
 
5
Other values (4)
 
14

Length

Max length42
Median length28
Mean length27.980822
Min length5

Characters and Unicode

Total characters40852
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWarranty Deed - Conventional
2nd rowWarranty Deed - Conventional
3rd rowWarranty Deed - Conventional
4th rowWarranty Deed - Conventional
5th rowWarranty Deed - Conventional

Common Values

ValueCountFrequency (%)
Warranty Deed - Conventional 1267
86.8%
Home just constructed and sold 122
 
8.4%
Court Officer Deed/Estate 43
 
2.9%
Contract Low Down 9
 
0.6%
Contract Low Interest 5
 
0.3%
Contract Low Down payment and low interest 5
 
0.3%
Warranty Deed - Cash 4
 
0.3%
Other 3
 
0.2%
Contract 15% Down payment regular terms 2
 
0.1%

Length

2025-01-27T18:28:22.365981image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T18:28:23.126177image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
warranty 1271
21.5%
1271
21.5%
deed 1271
21.5%
conventional 1267
21.4%
and 127
 
2.1%
home 122
 
2.1%
just 122
 
2.1%
constructed 122
 
2.1%
sold 122
 
2.1%
officer 43
 
0.7%
Other values (12) 177
 
3.0%

Most occurring characters

ValueCountFrequency (%)
n 5375
13.2%
4455
10.9%
e 4259
10.4%
a 4013
9.8%
t 3107
 
7.6%
o 3004
 
7.4%
r 2790
 
6.8%
d 1685
 
4.1%
l 1396
 
3.4%
C 1335
 
3.3%
Other values (25) 9433
23.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40852
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 5375
13.2%
4455
10.9%
e 4259
10.4%
a 4013
9.8%
t 3107
 
7.6%
o 3004
 
7.4%
r 2790
 
6.8%
d 1685
 
4.1%
l 1396
 
3.4%
C 1335
 
3.3%
Other values (25) 9433
23.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40852
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 5375
13.2%
4455
10.9%
e 4259
10.4%
a 4013
9.8%
t 3107
 
7.6%
o 3004
 
7.4%
r 2790
 
6.8%
d 1685
 
4.1%
l 1396
 
3.4%
C 1335
 
3.3%
Other values (25) 9433
23.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40852
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 5375
13.2%
4455
10.9%
e 4259
10.4%
a 4013
9.8%
t 3107
 
7.6%
o 3004
 
7.4%
r 2790
 
6.8%
d 1685
 
4.1%
l 1396
 
3.4%
C 1335
 
3.3%
Other values (25) 9433
23.1%

SaleCondition
Categorical

Imbalance 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
Normal Sale
1198 
Home was not completed when last assessed (associated with New Homes)
125 
Abnormal Sale - trade, foreclosure, short sale
 
101
Sale between family members
 
20
Allocation - two linked properties with separate deeds, typically condo with a garage unit
 
12

Length

Max length90
Median length11
Mean length19.357534
Min length11

Characters and Unicode

Total characters28262
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNormal Sale
2nd rowNormal Sale
3rd rowNormal Sale
4th rowAbnormal Sale - trade, foreclosure, short sale
5th rowNormal Sale

Common Values

ValueCountFrequency (%)
Normal Sale 1198
82.1%
Home was not completed when last assessed (associated with New Homes) 125
 
8.6%
Abnormal Sale - trade, foreclosure, short sale 101
 
6.9%
Sale between family members 20
 
1.4%
Allocation - two linked properties with separate deeds, typically condo with a garage unit 12
 
0.8%
Adjoining Land Purchase 4
 
0.3%

Length

2025-01-27T18:28:24.165954image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T18:28:24.955930image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
sale 1420
30.0%
normal 1198
25.3%
with 149
 
3.1%
home 125
 
2.6%
was 125
 
2.6%
not 125
 
2.6%
completed 125
 
2.6%
when 125
 
2.6%
last 125
 
2.6%
assessed 125
 
2.6%
Other values (25) 1096
23.1%

Most occurring characters

ValueCountFrequency (%)
a 3557
12.6%
3379
12.0%
l 3150
11.1%
e 3048
10.8%
o 2303
8.1%
r 1775
 
6.3%
m 1734
 
6.1%
s 1488
 
5.3%
N 1323
 
4.7%
S 1319
 
4.7%
Other values (23) 5186
18.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 28262
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3557
12.6%
3379
12.0%
l 3150
11.1%
e 3048
10.8%
o 2303
8.1%
r 1775
 
6.3%
m 1734
 
6.1%
s 1488
 
5.3%
N 1323
 
4.7%
S 1319
 
4.7%
Other values (23) 5186
18.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 28262
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3557
12.6%
3379
12.0%
l 3150
11.1%
e 3048
10.8%
o 2303
8.1%
r 1775
 
6.3%
m 1734
 
6.1%
s 1488
 
5.3%
N 1323
 
4.7%
S 1319
 
4.7%
Other values (23) 5186
18.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 28262
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3557
12.6%
3379
12.0%
l 3150
11.1%
e 3048
10.8%
o 2303
8.1%
r 1775
 
6.3%
m 1734
 
6.1%
s 1488
 
5.3%
N 1323
 
4.7%
S 1319
 
4.7%
Other values (23) 5186
18.3%

SalePrice
Real number (ℝ)

Distinct663
Distinct (%)45.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180921.2
Minimum34900
Maximum755000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2025-01-27T18:28:25.902995image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum34900
5-th percentile88000
Q1129975
median163000
Q3214000
95-th percentile326100
Maximum755000
Range720100
Interquartile range (IQR)84025

Descriptive statistics

Standard deviation79442.503
Coefficient of variation (CV)0.43910003
Kurtosis6.5362819
Mean180921.2
Median Absolute Deviation (MAD)38000
Skewness1.8828758
Sum2.6414495 × 108
Variance6.3111113 × 109
MonotonicityNot monotonic
2025-01-27T18:28:26.775999image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
140000 20
 
1.4%
135000 17
 
1.2%
155000 14
 
1.0%
145000 14
 
1.0%
190000 13
 
0.9%
110000 13
 
0.9%
115000 12
 
0.8%
160000 12
 
0.8%
130000 11
 
0.8%
139000 11
 
0.8%
Other values (653) 1323
90.6%
ValueCountFrequency (%)
34900 1
0.1%
35311 1
0.1%
37900 1
0.1%
39300 1
0.1%
40000 1
0.1%
52000 1
0.1%
52500 1
0.1%
55000 2
0.1%
55993 1
0.1%
58500 1
0.1%
ValueCountFrequency (%)
755000 1
0.1%
745000 1
0.1%
625000 1
0.1%
611657 1
0.1%
582933 1
0.1%
556581 1
0.1%
555000 1
0.1%
538000 1
0.1%
501837 1
0.1%
485000 1
0.1%

Missing values

2025-01-27T18:26:52.996246image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-27T18:26:54.076137image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-01-27T18:26:55.499691image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

MSSubClassMSZoningLotFrontageLotAreaStreetAlleyLotShapeLandContourUtilitiesLotConfigLandSlopeNeighborhoodCondition1Condition2BldgTypeHouseStyleOverallQualOverallCondYearBuiltYearRemodAddRoofStyleRoofMatlExterior1stExterior2ndMasVnrTypeMasVnrAreaExterQualExterCondFoundationBsmtQualBsmtCondBsmtExposureBsmtFinType1BsmtFinSF1BsmtFinType2BsmtFinSF2BsmtUnfSFTotalBsmtSFHeatingHeatingQCCentralAirElectrical1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBsmtFullBathBsmtHalfBathFullBathHalfBathBedroomAbvGrKitchenAbvGrKitchenQualTotRmsAbvGrdFunctionalFireplacesFireplaceQuGarageTypeGarageYrBltGarageFinishGarageCarsGarageAreaGarageQualGarageCondPavedDriveWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaPoolQCFenceMiscFeatureMiscValMoSoldYrSoldSaleTypeSaleConditionSalePrice
Id
12-STORY 1946 & NEWERResidential Low Density65.08450PavednanRegularNear Flat/LevelAll public Utilities (E,G,W,& S)Inside lotGentle slopeCollege CreekNormalNormalSingle-family Detached2StoryGoodAverage20032003GableStandard (Composite) ShingleVinyl SidingVinyl SidingBrick Face196.0GoodAverage/TypicalPoured ContreteGood (90-99 inches)Typical - slight dampness allowedNo ExposureGood Living Quarters706Unfinshed0150856Gas forced warm air furnaceExcellentYesStandard Circuit Breakers & Romex85685401710102131Good8Typical Functionality0nanAttached to home2003.0Rough Finished2548Typical/AverageTypical/AveragePaved0610000nannannan022008Warranty Deed - ConventionalNormal Sale208500
21-STORY 1946 & NEWER ALL STYLESResidential Low Density80.09600PavednanRegularNear Flat/LevelAll public Utilities (E,G,W,& S)Frontage on 2 sides of propertyGentle slopeVeenkerAdjacent to feeder streetNormalSingle-family DetachedOne storyAbove AverageVery Good19761976GableStandard (Composite) ShingleMetal SidingMetal Sidingnan0.0Average/TypicalAverage/TypicalCinder BlockGood (90-99 inches)Typical - slight dampness allowedGood ExposureAverage Living Quarters978Unfinshed02841262Gas forced warm air furnaceExcellentYesStandard Circuit Breakers & Romex1262001262012031Typical/Average6Typical Functionality1Average - Prefabricated Fireplace in main living area or Masonry Fireplace in basementAttached to home1976.0Rough Finished2460Typical/AverageTypical/AveragePaved29800000nannannan052007Warranty Deed - ConventionalNormal Sale181500
32-STORY 1946 & NEWERResidential Low Density68.011250PavednanSlightly irregularNear Flat/LevelAll public Utilities (E,G,W,& S)Inside lotGentle slopeCollege CreekNormalNormalSingle-family Detached2StoryGoodAverage20012002GableStandard (Composite) ShingleVinyl SidingVinyl SidingBrick Face162.0GoodAverage/TypicalPoured ContreteGood (90-99 inches)Typical - slight dampness allowedMimimum ExposureGood Living Quarters486Unfinshed0434920Gas forced warm air furnaceExcellentYesStandard Circuit Breakers & Romex92086601786102131Good6Typical Functionality1Average - Prefabricated Fireplace in main living area or Masonry Fireplace in basementAttached to home2001.0Rough Finished2608Typical/AverageTypical/AveragePaved0420000nannannan092008Warranty Deed - ConventionalNormal Sale223500
42-STORY 1945 & OLDERResidential Low Density60.09550PavednanSlightly irregularNear Flat/LevelAll public Utilities (E,G,W,& S)Corner lotGentle slopeCrawfordNormalNormalSingle-family Detached2StoryGoodAverage19151970GableStandard (Composite) ShingleWood SidingWd Shngnan0.0Average/TypicalAverage/TypicalBrick & TileTypical (80-89 inches)GoodNo ExposureAverage Living Quarters216Unfinshed0540756Gas forced warm air furnaceGoodYesStandard Circuit Breakers & Romex96175601717101031Good7Typical Functionality1Good - Masonry Fireplace in main levelDetached from home1998.0Unfinished3642Typical/AverageTypical/AveragePaved035272000nannannan022006Warranty Deed - ConventionalAbnormal Sale - trade, foreclosure, short sale140000
52-STORY 1946 & NEWERResidential Low Density84.014260PavednanSlightly irregularNear Flat/LevelAll public Utilities (E,G,W,& S)Frontage on 2 sides of propertyGentle slopeNorthridgeNormalNormalSingle-family Detached2StoryVery GoodAverage20002000GableStandard (Composite) ShingleVinyl SidingVinyl SidingBrick Face350.0GoodAverage/TypicalPoured ContreteGood (90-99 inches)Typical - slight dampness allowedAverage Exposure (split levels or foyers typically score average or above)Good Living Quarters655Unfinshed04901145Gas forced warm air furnaceExcellentYesStandard Circuit Breakers & Romex1145105302198102141Good9Typical Functionality1Average - Prefabricated Fireplace in main living area or Masonry Fireplace in basementAttached to home2000.0Rough Finished3836Typical/AverageTypical/AveragePaved192840000nannannan0122008Warranty Deed - ConventionalNormal Sale250000
61-1/2 STORY FINISHED ALL AGESResidential Low Density85.014115PavednanSlightly irregularNear Flat/LevelAll public Utilities (E,G,W,& S)Inside lotGentle slopeMitchellNormalNormalSingle-family Detached1.5FinAverageAverage19931995GableStandard (Composite) ShingleVinyl SidingVinyl Sidingnan0.0Average/TypicalAverage/TypicalWoodGood (90-99 inches)Typical - slight dampness allowedNo ExposureGood Living Quarters732Unfinshed064796Gas forced warm air furnaceExcellentYesStandard Circuit Breakers & Romex79656601362101111Typical/Average5Typical Functionality0nanAttached to home1993.0Unfinished2480Typical/AverageTypical/AveragePaved4030032000nanMinimum PrivacyShed (over 100 SF)700102009Warranty Deed - ConventionalNormal Sale143000
71-STORY 1946 & NEWER ALL STYLESResidential Low Density75.010084PavednanRegularNear Flat/LevelAll public Utilities (E,G,W,& S)Inside lotGentle slopeSomersetNormalNormalSingle-family DetachedOne storyVery GoodAverage20042005GableStandard (Composite) ShingleVinyl SidingVinyl SidingStone186.0GoodAverage/TypicalPoured ContreteExcellent (100+ inches)Typical - slight dampness allowedAverage Exposure (split levels or foyers typically score average or above)Good Living Quarters1369Unfinshed03171686Gas forced warm air furnaceExcellentYesStandard Circuit Breakers & Romex1694001694102031Good7Typical Functionality1Good - Masonry Fireplace in main levelAttached to home2004.0Rough Finished2636Typical/AverageTypical/AveragePaved255570000nannannan082007Warranty Deed - ConventionalNormal Sale307000
82-STORY 1946 & NEWERResidential Low DensityNaN10382PavednanSlightly irregularNear Flat/LevelAll public Utilities (E,G,W,& S)Corner lotGentle slopeNorthwest AmesNear positive off-site feature--park, greenbelt, etc.NormalSingle-family Detached2StoryGoodAbove Average19731973GableStandard (Composite) ShingleHard BoardHard BoardStone240.0Average/TypicalAverage/TypicalCinder BlockGood (90-99 inches)Typical - slight dampness allowedMimimum ExposureAverage Living Quarters859Below Average Living Quarters322161107Gas forced warm air furnaceExcellentYesStandard Circuit Breakers & Romex110798302090102131Typical/Average7Typical Functionality2Average - Prefabricated Fireplace in main living area or Masonry Fireplace in basementAttached to home1973.0Rough Finished2484Typical/AverageTypical/AveragePaved235204228000nannanShed (over 100 SF)350112009Warranty Deed - ConventionalNormal Sale200000
91-1/2 STORY FINISHED ALL AGESResidential Medium Density51.06120PavednanRegularNear Flat/LevelAll public Utilities (E,G,W,& S)Inside lotGentle slopeOld TownAdjacent to arterial streetNormalSingle-family Detached1.5FinGoodAverage19311950GableStandard (Composite) ShingleBrick FaceWd Shngnan0.0Average/TypicalAverage/TypicalBrick & TileTypical (80-89 inches)Typical - slight dampness allowedNo ExposureUnfinshed0Unfinshed0952952Gas forced warm air furnaceGoodYes60 AMP Fuse Box and mostly Romex wiring (Fair)102275201774002022Typical/Average8Minor Deductions 12Average - Prefabricated Fireplace in main living area or Masonry Fireplace in basementDetached from home1931.0Unfinished2468FairTypical/AveragePaved900205000nannannan042008Warranty Deed - ConventionalAbnormal Sale - trade, foreclosure, short sale129900
102 FAMILY CONVERSION - ALL STYLES AND AGESResidential Low Density50.07420PavednanRegularNear Flat/LevelAll public Utilities (E,G,W,& S)Corner lotGentle slopeBrooksideAdjacent to arterial streetAdjacent to arterial street2fmCon1.5UnfAverageAbove Average19391950GableStandard (Composite) ShingleMetal SidingMetal Sidingnan0.0Average/TypicalAverage/TypicalBrick & TileTypical (80-89 inches)Typical - slight dampness allowedNo ExposureGood Living Quarters851Unfinshed0140991Gas forced warm air furnaceExcellentYesStandard Circuit Breakers & Romex1077001077101022Typical/Average5Typical Functionality2Average - Prefabricated Fireplace in main living area or Masonry Fireplace in basementAttached to home1939.0Rough Finished1205GoodTypical/AveragePaved040000nannannan012008Warranty Deed - ConventionalNormal Sale118000
MSSubClassMSZoningLotFrontageLotAreaStreetAlleyLotShapeLandContourUtilitiesLotConfigLandSlopeNeighborhoodCondition1Condition2BldgTypeHouseStyleOverallQualOverallCondYearBuiltYearRemodAddRoofStyleRoofMatlExterior1stExterior2ndMasVnrTypeMasVnrAreaExterQualExterCondFoundationBsmtQualBsmtCondBsmtExposureBsmtFinType1BsmtFinSF1BsmtFinType2BsmtFinSF2BsmtUnfSFTotalBsmtSFHeatingHeatingQCCentralAirElectrical1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBsmtFullBathBsmtHalfBathFullBathHalfBathBedroomAbvGrKitchenAbvGrKitchenQualTotRmsAbvGrdFunctionalFireplacesFireplaceQuGarageTypeGarageYrBltGarageFinishGarageCarsGarageAreaGarageQualGarageCondPavedDriveWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaPoolQCFenceMiscFeatureMiscValMoSoldYrSoldSaleTypeSaleConditionSalePrice
Id
1451DUPLEX - ALL STYLES AND AGESResidential Low Density60.09000PavednanRegularNear Flat/LevelAll public Utilities (E,G,W,& S)Frontage on 2 sides of propertyGentle slopeNorth AmesNormalNormalDuplex2StoryAverageAverage19741974GableStandard (Composite) ShingleVinyl SidingVinyl Sidingnan0.0Average/TypicalAverage/TypicalCinder BlockGood (90-99 inches)Typical - slight dampness allowedNo ExposureUnfinshed0Unfinshed0896896Gas forced warm air furnaceAverage/TypicalYesStandard Circuit Breakers & Romex89689601792002242Typical/Average8Typical Functionality0nannanNaNnan00nannanPaved32450000nannannan092009Warranty Deed - ConventionalNormal Sale136000
14521-STORY 1946 & NEWER ALL STYLESResidential Low Density78.09262PavednanRegularNear Flat/LevelAll public Utilities (E,G,W,& S)Inside lotGentle slopeSomersetNormalNormalSingle-family DetachedOne storyVery GoodAverage20082009GableStandard (Composite) ShingleCement BoardCmentBdStone194.0GoodAverage/TypicalPoured ContreteGood (90-99 inches)Typical - slight dampness allowedNo ExposureUnfinshed0Unfinshed015731573Gas forced warm air furnaceExcellentYesStandard Circuit Breakers & Romex1578001578002031Excellent7Typical Functionality1Good - Masonry Fireplace in main levelAttached to home2008.0Finished3840Typical/AverageTypical/AveragePaved0360000nannannan052009Home just constructed and soldHome was not completed when last assessed (associated with New Homes)287090
1453PUD - MULTILEVEL - INCL SPLIT LEV/FOYERResidential Medium Density35.03675PavednanRegularNear Flat/LevelAll public Utilities (E,G,W,& S)Inside lotGentle slopeEdwardsNormalNormalTownhouse End UnitSLvlAverageAverage20052005GableStandard (Composite) ShingleVinyl SidingVinyl SidingBrick Face80.0Average/TypicalAverage/TypicalPoured ContreteGood (90-99 inches)Typical - slight dampness allowedGood ExposureGood Living Quarters547Unfinshed00547Gas forced warm air furnaceGoodYesStandard Circuit Breakers & Romex1072001072101021Typical/Average5Typical Functionality0nanBasement Garage2005.0Finished2525Typical/AverageTypical/AveragePaved0280000nannannan052006Warranty Deed - ConventionalNormal Sale145000
14541-STORY 1946 & NEWER ALL STYLESResidential Low Density90.017217PavednanRegularNear Flat/LevelAll public Utilities (E,G,W,& S)Inside lotGentle slopeMitchellNormalNormalSingle-family DetachedOne storyAverageAverage20062006GableStandard (Composite) ShingleVinyl SidingVinyl Sidingnan0.0Average/TypicalAverage/TypicalPoured ContreteGood (90-99 inches)Typical - slight dampness allowedNo ExposureUnfinshed0Unfinshed011401140Gas forced warm air furnaceExcellentYesStandard Circuit Breakers & Romex1140001140001031Typical/Average6Typical Functionality0nannanNaNnan00nannanPaved36560000nannannan072006Warranty Deed - ConventionalAbnormal Sale - trade, foreclosure, short sale84500
14551-STORY 1946 & NEWER ALL STYLESFloating Village Residential62.07500PavedPavedRegularNear Flat/LevelAll public Utilities (E,G,W,& S)Inside lotGentle slopeSomersetNormalNormalSingle-family DetachedOne storyGoodAverage20042005GableStandard (Composite) ShingleVinyl SidingVinyl Sidingnan0.0GoodAverage/TypicalPoured ContreteGood (90-99 inches)Typical - slight dampness allowedNo ExposureGood Living Quarters410Unfinshed08111221Gas forced warm air furnaceExcellentYesStandard Circuit Breakers & Romex1221001221102021Good6Typical Functionality0nanAttached to home2004.0Rough Finished2400Typical/AverageTypical/AveragePaved01130000nannannan0102009Warranty Deed - ConventionalNormal Sale185000
14562-STORY 1946 & NEWERResidential Low Density62.07917PavednanRegularNear Flat/LevelAll public Utilities (E,G,W,& S)Inside lotGentle slopeGilbertNormalNormalSingle-family Detached2StoryAbove AverageAverage19992000GableStandard (Composite) ShingleVinyl SidingVinyl Sidingnan0.0Average/TypicalAverage/TypicalPoured ContreteGood (90-99 inches)Typical - slight dampness allowedNo ExposureUnfinshed0Unfinshed0953953Gas forced warm air furnaceExcellentYesStandard Circuit Breakers & Romex95369401647002131Typical/Average7Typical Functionality1Average - Prefabricated Fireplace in main living area or Masonry Fireplace in basementAttached to home1999.0Rough Finished2460Typical/AverageTypical/AveragePaved0400000nannannan082007Warranty Deed - ConventionalNormal Sale175000
14571-STORY 1946 & NEWER ALL STYLESResidential Low Density85.013175PavednanRegularNear Flat/LevelAll public Utilities (E,G,W,& S)Inside lotGentle slopeNorthwest AmesNormalNormalSingle-family DetachedOne storyAbove AverageAbove Average19781988GableStandard (Composite) ShinglePlywoodPlywoodStone119.0Average/TypicalAverage/TypicalCinder BlockGood (90-99 inches)Typical - slight dampness allowedNo ExposureAverage Living Quarters790Average Rec Room1635891542Gas forced warm air furnaceAverage/TypicalYesStandard Circuit Breakers & Romex2073002073102031Typical/Average7Minor Deductions 12Average - Prefabricated Fireplace in main living area or Masonry Fireplace in basementAttached to home1978.0Unfinished2500Typical/AverageTypical/AveragePaved34900000nanMinimum Privacynan022010Warranty Deed - ConventionalNormal Sale210000
14582-STORY 1945 & OLDERResidential Low Density66.09042PavednanRegularNear Flat/LevelAll public Utilities (E,G,W,& S)Inside lotGentle slopeCrawfordNormalNormalSingle-family Detached2StoryGoodExcellent19412006GableStandard (Composite) ShingleCement BoardCmentBdnan0.0ExcellentGoodStoneTypical (80-89 inches)GoodNo ExposureGood Living Quarters275Unfinshed08771152Gas forced warm air furnaceExcellentYesStandard Circuit Breakers & Romex1188115202340002041Good9Typical Functionality2Good - Masonry Fireplace in main levelAttached to home1941.0Rough Finished1252Typical/AverageTypical/AveragePaved0600000nanGood PrivacyShed (over 100 SF)250052010Warranty Deed - ConventionalNormal Sale266500
14591-STORY 1946 & NEWER ALL STYLESResidential Low Density68.09717PavednanRegularNear Flat/LevelAll public Utilities (E,G,W,& S)Inside lotGentle slopeNorth AmesNormalNormalSingle-family DetachedOne storyAverageAbove Average19501996HipStandard (Composite) ShingleMetal SidingMetal Sidingnan0.0Average/TypicalAverage/TypicalCinder BlockTypical (80-89 inches)Typical - slight dampness allowedMimimum ExposureGood Living Quarters49Average Rec Room102901078Gas forced warm air furnaceGoodYesFuse Box over 60 AMP and all Romex wiring (Average)1078001078101021Good5Typical Functionality0nanAttached to home1950.0Unfinished1240Typical/AverageTypical/AveragePaved3660112000nannannan042010Warranty Deed - ConventionalNormal Sale142125
14601-STORY 1946 & NEWER ALL STYLESResidential Low Density75.09937PavednanRegularNear Flat/LevelAll public Utilities (E,G,W,& S)Inside lotGentle slopeEdwardsNormalNormalSingle-family DetachedOne storyAverageAbove Average19651965GableStandard (Composite) ShingleHard BoardHard Boardnan0.0GoodAverage/TypicalCinder BlockTypical (80-89 inches)Typical - slight dampness allowedNo ExposureBelow Average Living Quarters830Low Quality2901361256Gas forced warm air furnaceGoodYesStandard Circuit Breakers & Romex1256001256101131Typical/Average6Typical Functionality0nanAttached to home1965.0Finished1276Typical/AverageTypical/AveragePaved736680000nannannan062008Warranty Deed - ConventionalNormal Sale147500